GROOT FORCE Test Documentation

Variant-Specific Test Cases

Document Version: 1.0
Last Updated: 2025-11-24
Status: Production Ready
Test Phase: Phase 6 - Variant Testing

Document Purpose

This document contains comprehensive test cases specific to each GROOT FORCE product variant. Each variant has unique hardware configurations, software features, and target use cases requiring specialized validation beyond the core platform tests.

Test Scope

9 Product Variants with 5 test cases each
45 Total Test Procedures
Focus on variant-unique features and target persona requirements
Assumes core platform tests (Phases 1-5) have passed

Variant Overview

Variant Code	Name	Target Persona	Key Differentiators
GF-BE	Ben's Assistive	Mobility-impaired users	Wheelchair mounting, voice/gaze control
GF-CL	Care & Joy	NDIS support workers	Note-taking, consent management, compliance
GF-NF	Nick's Fitness	Athletes & fitness enthusiasts	Advanced health sensors, performance tracking
GF-TX	TradeForce Pro	Trades & industrial workers	IP65 protection, OH&S, AR measurement
GF-DI	SilentLink	Deaf/hard-of-hearing	Live captioning, sound alerts, directional audio
GF-VI	VisionAssist	Low-vision/blind users	Scene description, OCR navigation, haptic guidance
GF-TR	Traveller Edition	International travelers	Translation 24+ languages, currency, offline maps
GF-LX	Lifestyle Edition	Consumer wellness market	Journaling, mood tracking, general AI assistant
GF-EN	Enterprise Vision	Industrial fleets	SOP integration, remote expert, fleet management

1. GF-BE: Ben's Assistive Variant

Target User: Individuals with mobility impairments, wheelchair users, limited hand mobility

TC-VAR-BE-001: Wheelchair Mounting System Durability

Priority: P1 - Critical
Test Type: Hardware - Mechanical
Estimated Duration: 8 hours
Prerequisites: GF-BE variant with wheelchair mounting bracket

Objective

Validate that the wheelchair mounting system securely holds the glasses and withstands typical wheelchair use scenarios without failure.

Test Equipment

Standard manual wheelchair (24-26" wheels)
Powered wheelchair (if available)
Vibration measurement device
Force gauge (0-50N range)
High-speed camera
Test course with varied terrain

Test Procedure

Step 1: Mounting System Installation

Install wheelchair mounting bracket per assembly instructions
Verify secure attachment to wheelchair frame/armrest
Measure installation time (should be ≤5 minutes)
Check mounting does not interfere with wheelchair operation
Verify mounting supports weight of glasses (55-58g) + 20% safety margin

Step 2: Static Load Testing

Mount GF-BE glasses to bracket
Apply downward force in 5N increments up to 20N
Verify no detachment, cracking, or permanent deformation
Check glasses remain properly aligned
Measure retention force required to remove glasses (should be 8-15N)

Step 3: Dynamic Vibration Testing

Wheelchair user performs 30-minute session including:
- Indoor smooth floors (10 min)
- Outdoor pavement (10 min)
- Rough surfaces/cracks (5 min)
- Ramps up/down (5 min)
Record vibration levels at mounting point
Verify glasses do not detach or shift position
Check for visible wear or stress on mounting components

Step 4: Durability Cycling

Perform 100 mount/dismount cycles
Measure retention force every 25 cycles
Verify mounting mechanism does not loosen
Check for material fatigue or wear indicators

Step 5: Emergency Quick-Release

Test quick-release mechanism (if present)
Verify release force is safe (15-30N)
Ensure release can be performed single-handed
Check glasses safely retained until release engaged

Pass Criteria

✅ Installation time ≤5 minutes
✅ Withstands 20N static load without failure
✅ No detachment during 30-minute dynamic use
✅ Retention force stable 8-15N across 100 cycles
✅ Vibration levels ≤2g RMS at mounting point
✅ Quick-release functional and safe
✅ No visible damage after durability testing

Failure Modes to Monitor

Mounting bracket loosens from wheelchair
Glasses detach during vibration
Mounting mechanism wears excessively
Quick-release fails or activates accidentally
Mounting interferes with wheelchair control

TC-VAR-BE-002: Voice & Gaze Control Accuracy

Priority: P1 - Critical
Test Type: Software - Accessibility
Estimated Duration: 4 hours
Prerequisites: GF-BE with voice control and gaze tracking enabled

Objective

Validate that voice commands and gaze-based control work reliably for users with limited hand mobility, providing full device functionality without touch input.

Test Equipment

Calibrated speech recording setup
Eye-tracking validation system (if available)
3 test users with varying speech patterns
List of 50 common voice commands
Typical usage environment (home/office)

Test Procedure

Step 1: Voice Command Recognition Baseline

Test user speaks each command once in quiet environment
Measure recognition accuracy per command
Test commands include:
- Navigation: "Go home", "Open settings", "Back"
- Control: "Increase brightness", "Volume up/down"
- AI interaction: "Hey KLYRA", "Take note", "Read message"
- Emergency: "Call help", "SOS"
- Mode switching: "Walk assist mode", "Reading mode"
Record accuracy and false rejection rate

Step 2: Voice Control in Noisy Environment

Introduce background noise at 65 dB (typical home/office)
Repeat all 50 commands
Measure recognition accuracy degradation
Test beamforming microphone effectiveness
Verify emergency commands prioritized

Step 3: Speaker-Specific Adaptation

User performs voice training (if required)
Test recognition improvement after training
Verify system adapts to user's specific speech patterns
Test with motor speech impairments (if applicable):
- Dysarthria (slurred speech)
- Apraxia (difficulty sequencing sounds)
- Slow/deliberate speech patterns

Step 4: Gaze-Based Navigation

Calibrate gaze tracking system
User navigates UI using gaze + dwell (1.5 sec default)
Test gaze accuracy across different screen zones
Measure selection accuracy and false positives
Test multi-step tasks using gaze only:
- Compose message using on-screen keyboard
- Adjust system settings
- Launch application

Step 5: Combined Voice + Gaze Workflow

User performs complete tasks using both modalities:
- Voice: "Open messages"
- Gaze: Select conversation
- Voice: "Read message"
- Gaze: Select reply option
- Voice: Dictate reply
- Gaze: Confirm send
Measure task completion time vs touch baseline
Verify no mode confusion or interface conflicts

Step 6: Fatigue & Extended Use

User performs 30-minute continuous interaction
Monitor recognition accuracy over time
Check for voice fatigue affecting performance
Verify gaze tracking remains calibrated

Pass Criteria

✅ Voice recognition ≥95% quiet, ≥90% noisy (65 dB)
✅ Emergency commands 100% recognition
✅ Gaze selection accuracy ≥92%
✅ Gaze false positive rate ≤3%
✅ Task completion time ≤150% of touch baseline
✅ No accuracy degradation after 30 minutes
✅ Works with motor speech impairments (≥85% accuracy)
✅ User satisfaction rating ≥4.0/5.0

Failure Modes to Monitor

Voice recognition fails in noise
Gaze tracking loses calibration
Dwell time too short/long causes errors
Emergency commands not prioritized
System requires hand input for critical functions

TC-VAR-BE-003: Hazard Alert & Proximity Warning System

Priority: P1 - Critical
Test Type: Software - Safety
Estimated Duration: 3 hours
Prerequisites: GF-BE with ToF, LiDAR, and hazard detection enabled

Objective

Validate that hazard detection and proximity warnings provide timely alerts for wheelchair users approaching obstacles, curbs, and environmental hazards.

Test Equipment

Wheelchair (manual or powered)
Various obstacles: walls, curbs, steps, furniture, people
Obstacle positioning measuring tape
Timer for latency measurement
Test environment with controlled hazards

Test Procedure

Step 1: Forward Obstacle Detection

Wheelchair approaches wall at 0.5 m/s
Record warning distance (should be 2-3m ahead)
Verify warning escalates as distance decreases:
- 3m: Visual HUD indicator
- 2m: Haptic vibration + HUD
- 1m: Audio warning + haptic + HUD
Test with various obstacles:
- Solid walls
- Glass barriers
- People (moving/stationary)
- Furniture at different heights
Measure detection latency (should be ≤200ms)

Step 2: Curb & Step-Down Detection

Approach curb or step-down at 0.3 m/s
Verify warning triggered ≥1.5m before edge
Warning should indicate:
- Direction (left/right/center)
- Severity (step height if measurable)
- Urgency escalation
Test various drop heights:
- 5cm (small curb)
- 15cm (standard curb)
- 30cm+ (stairs)
Verify no false negatives on critical drops

Step 3: Side Obstacle Detection

Navigate narrow corridors (90cm width)
Verify side clearance warnings
Test doorway navigation assistance
Measure lateral detection range (should be ±60cm)
Check warning directivity (left vs right)

Step 4: Overhead Hazard Detection

Approach low-hanging obstacles (doorframes, branches)
If LiDAR mounted appropriately, verify detection
Test height detection range 150-200cm
Verify appropriate for seated wheelchair position

Step 5: Multi-Hazard Scenarios

Complex environment with multiple hazards
Verify system prioritizes most critical threat
Test warning clarity when multiple alerts active
Ensure system doesn't overwhelm user with alerts

Step 6: Alert Customization & Sensitivity

Test adjustable sensitivity settings (low/medium/high)
Verify custom alert preferences respected:
- Audio on/off
- Haptic intensity
- Warning distances
Test "quiet mode" for familiar environments

Pass Criteria

✅ Forward obstacle detection ≥2m at 0.5 m/s speed
✅ Detection latency ≤200ms
✅ Curb/step warning ≥1.5m ahead
✅ Side clearance detection ±60cm
✅ Detection rate ≥98% for critical hazards
✅ False positive rate ≤5% in typical environments
✅ Warning escalation clear and timely
✅ Multi-hazard prioritization logical
✅ User can customize alert preferences

Failure Modes to Monitor

Fails to detect transparent barriers (glass)
Late warning insufficient for stopping
False positives cause alert fatigue
Curb detection misses critical drops
Lateral detection misses narrow clearances

TC-VAR-BE-004: Remote Assistance & SOS Integration

Priority: P1 - Critical
Test Type: Software - Emergency Response
Estimated Duration: 2 hours
Prerequisites: GF-BE with network connectivity and remote assistance app

Objective

Validate that remote assistance features enable caregivers or emergency services to receive alerts, view user status, and provide two-way communication during emergencies.

Test Equipment

GF-BE device
Caregiver smartphone with companion app
Emergency contact list configured
Test Wi-Fi and cellular networks
GPS-enabled environment

Test Procedure

Step 1: SOS Activation & Alert Delivery

User activates SOS (voice: "SOS" or triple-press button)
Verify immediate alert sent to all emergency contacts
Measure alert delivery latency (should be ≤5 seconds)
Verify alert includes:
- GPS location (±10m accuracy)
- Battery status
- Last known orientation (standing/fallen)
- Timestamp
Test with network connectivity:
- Wi-Fi only
- Cellular only
- Offline → queued for send

Step 2: Two-Way Audio Communication

Caregiver receives SOS alert
Initiate two-way audio call from app
Verify audio quality in both directions
Test bone conduction clarity for responder
Measure call establishment time (≤10 seconds)
Test call reliability across 5-minute duration

Step 3: Remote Video View (if enabled)

Caregiver requests camera view
User must confirm or auto-accept after 10 sec delay
Verify low-latency video stream (≤1 sec delay)
Test video quality sufficient to assess situation
Verify privacy controls prevent unauthorized access

Step 4: Location Tracking & Updates

During active assistance session
Verify location updates every 30 seconds
Test location accuracy indoor/outdoor
Check breadcrumb trail shows movement
Verify location sharing stops when session ends

Step 5: Fall Detection Integration

Simulate fall event (controlled environment)
Verify automatic SOS triggered after 30 sec no response
Test user can cancel false alarm within 30 sec
Verify fall alert includes:
- "Fall detected" flag
- Impact severity estimate (if available)
- Post-fall orientation

Step 6: Multiple Emergency Contacts

Configure 3 emergency contacts with priority
Test cascading alert system:
- Primary contact alerted first
- If no response in 60 sec, alert secondary
- Continue until acknowledgment received
Verify all contacts notified simultaneously for critical events

Step 7: Emergency Services Integration

Test direct 911/000 calling (if supported)
Verify location and user info provided to dispatcher
Test medical information sharing (if configured):
- Allergies
- Medications
- Medical conditions
- Emergency contacts

Pass Criteria

✅ SOS alert delivery ≤5 seconds
✅ GPS location accuracy ≤10m outdoor, ≤50m indoor
✅ Two-way audio call establishes ≤10 seconds
✅ Audio quality clear both directions
✅ Fall detection triggers SOS automatically
✅ Privacy controls functional and respected
✅ Cascading alerts work correctly
✅ Works on Wi-Fi and cellular networks
✅ Emergency info accessible to responders

Failure Modes to Monitor

Alert fails to send without notification
Location inaccurate or stale
Audio call drops or quality poor
Privacy controls bypassed
Fall detection doesn't trigger SOS
Battery drains too quickly during session

Priority: P1 - Critical
Test Type: Software - User Interface
Estimated Duration: 3 hours
Prerequisites: GF-BE with all accessibility features enabled

Objective

Validate that the UI meets accessibility standards for users with limited mobility, providing clear visual feedback, simplified navigation, and customizable interface options.

Test Equipment

GF-BE device
Users with varying mobility limitations (3 testers)
Accessibility evaluation checklist (WCAG 2.1 AA)
Task completion time measurement

Test Procedure

Step 1: High-Contrast & Large Text Mode

Enable high-contrast mode
Verify contrast ratios meet WCAG 2.1 AA (≥4.5:1 normal text, ≥3:1 large text)
Test large text mode (font sizes 18-24pt)
Verify all UI elements readable in bright outdoor light
Test with users having low vision

Step 2: Simplified Menu Navigation

Count menu depth (should be ≤2 levels for common tasks)
Verify large, easy-to-target UI elements (≥44dp touch targets)
Test voice navigation through all menus
Measure task completion time for common actions:
- Make call: ≤30 sec
- Send message: ≤45 sec
- Change settings: ≤40 sec
Compare vs standard GF-LX interface (should be 20-30% faster)

Step 3: Dwell-Click & Alternative Selection

Test gaze + dwell selection (1.5 sec default dwell time)
Verify visual feedback during dwell countdown
Test adjustable dwell time (0.8-3.0 sec range)
Verify false activations minimized
Test voice confirmation for critical actions

Step 4: Persistent Toolbar & Quick Actions

Verify quick-access toolbar always visible
Test customizable quick actions (user can set 4-6 shortcuts)
Common actions should include:
- SOS
- Call primary contact
- Hazard detection on/off
- Brightness
- Volume
Verify toolbar doesn't obstruct main content

Step 5: Audio & Haptic Feedback

Test audio feedback for all interactions
Verify haptic confirmation for selections
Test feedback customization:
- Volume levels
- Haptic intensity
- Feedback types (click/whoosh/tone)
Ensure feedback doesn't become annoying

Step 6: Error Prevention & Recovery

Test confirmation dialogs for critical actions:
- Delete data
- Change settings
- Make calls
Verify easy undo for accidental actions
Test "Are you sure?" dialogs for destructive actions
Verify clear error messages with recovery options

Step 7: Customization Persistence

Configure all accessibility settings
Reboot device
Verify all settings persist across restarts
Test settings sync to cloud backup (if enabled)
Verify settings restore after factory reset (from backup)

Pass Criteria

✅ Contrast ratios meet WCAG 2.1 AA
✅ Text readable in bright light at 1,200 nits
✅ Menu depth ≤2 levels for 90% of tasks
✅ Task completion 20-30% faster than standard UI
✅ Touch targets ≥44dp (minimum)
✅ Dwell-click functional with ≤2% false positives
✅ All settings customizable and persistent
✅ Audio and haptic feedback clear and helpful
✅ Error prevention effective (accidental actions < 5%)
✅ User satisfaction ≥4.2/5.0

Failure Modes to Monitor

Low contrast makes text unreadable
Menu navigation confusing or deep
Touch targets too small for accurate selection
Dwell time too short/long causes errors
Settings don't persist across reboots
Feedback annoying or insufficient

2. GF-CL: Care & Joy (NDIS) Variant

Target User: NDIS support workers, care providers, disability support professionals

TC-VAR-CL-001: NDIS Progress Note Generation

Priority: P1 - Critical
Test Type: Software - Documentation
Estimated Duration: 4 hours
Prerequisites: GF-CL with NDIS note-taking module enabled

Objective

Validate that the NDIS progress note generation system accurately transcribes support sessions, structures notes per NDIS standards, and produces compliant documentation.

Test Equipment

GF-CL device
Test recording scenarios (3 support sessions)
NDIS Quality and Safeguards Commission documentation standards
Sample compliant vs non-compliant notes
Support worker testers (2-3)

Test Procedure

Step 1: Session Recording & Transcription

Support worker initiates session recording (voice: "Start session")
Perform 30-minute mock support session including:
- Participant introduction
- Activities undertaken (meal prep, exercise, community access)
- Participant responses and engagement
- Support provided
- Observations and concerns
- Outcomes and next steps
Verify continuous recording with battery indicator
Test background noise handling (typical home environment)
Verify recording stops on command (voice: "End session")

Step 2: Automatic Note Structuring

AI analyzes recording and generates structured note
Verify note includes required NDIS sections:
- Participant name and NDIS number (if configured)
- Date and time of support
- Support worker name
- Duration of support
- Location of support
- Goals addressed (linked to participant's plan)
- Activities undertaken
- Support provided
- Participant's response and engagement
- Progress toward goals
- Any incidents or concerns
- Next steps and follow-up required
Measure generation time (should be ≤2 minutes for 30-min session)

Step 3: Accuracy & Quality Assessment

Support worker reviews AI-generated note
Compare against manual note taken during session
Assess accuracy across categories:
- Factual accuracy: ≥95% (events, times, activities correct)
- Completeness: All key points captured
- Professional language: Appropriate terminology
- Person-centered language: Respectful and empowering
- NDIS compliance: Meets documentation standards
Count errors requiring correction (should be ≤3 per note)

Step 4: Editing & Finalization

Support worker edits note using voice/text
Test voice editing commands:
- "Add to activities section: ..."
- "Change participant response to: ..."
- "Remove that last sentence"
Verify edits accurately applied
Test note preview and formatting
Finalize note and export as PDF

Step 5: Privacy & Consent Handling

Verify recording requires explicit consent
Test consent prompt: "Do you consent to recording for documentation?"
If consent declined, verify note-taking disabled
Verify participant can request recording stopped at any time
Test recording indicator always visible (red dot/icon)

Step 6: NDIS-Specific Features

Test goal linking feature:
- Support worker says "This addresses goal 3"
- Verify AI links activity to pre-configured goal
Test incident flagging:
- Support worker mentions concern or incident
- Verify system flags for follow-up and separate incident report
Test multi-participant sessions (if applicable):
- Multiple participants in group activity
- Verify separate notes generated per participant

Step 7: Export & Integration

Export note as PDF with proper formatting
Verify PDF includes:
- All required NDIS sections
- Professional formatting
- Support worker digital signature (if configured)
- Participant signature field (if required)
Test integration with NDIS management software (if available):
- Exported to Lumary, SupportAbility, or similar platforms
Verify encrypted storage of notes on device

Pass Criteria

✅ Recording quality sufficient for ≥95% transcription accuracy
✅ Note generation completes ≤2 minutes
✅ All required NDIS sections included automatically
✅ Factual accuracy ≥95%
✅ Professional and person-centered language used
✅ Errors requiring correction ≤3 per note
✅ Consent handling 100% compliant
✅ Recording indicator always visible
✅ Goal linking functional and accurate
✅ PDF export formatted correctly
✅ Support worker satisfaction ≥4.5/5.0

Failure Modes to Monitor

Poor transcription in noisy environments
Missing required NDIS sections
Unprofessional or inappropriate language
Consent not properly obtained
Recording fails without notification
Note generation takes too long
Export fails or formatting incorrect

Priority: P1 - Critical
Test Type: Software - Privacy & Ethics
Estimated Duration: 2 hours
Prerequisites: GF-CL with consent management system enabled

Objective

Validate that the consent management system properly obtains, records, and respects participant consent for recording, photography, and data sharing per NDIS requirements.

Test Equipment

GF-CL device
Mock participant consent scenarios
NDIS privacy framework reference
Test participants (2-3)

Test Procedure

Step 1: Initial Consent Prompts

First-time use with new participant
Verify system prompts for consent before any recording
Test consent prompt clarity and language:
- "Do you consent to voice recording for documentation?"
- "Do you consent to photos being taken during this session?"
- "Do you consent to data being stored in the cloud?" (if applicable)
Verify participant can understand and respond easily
Test multiple response methods:
- Voice: "Yes" / "No" / "I consent" / "I do not consent"
- Button press: Green = consent, Red = decline
- Caregiver can provide consent on behalf (if appropriate)

Step 2: Consent Recording & Documentation

Verify verbal consent recorded as audio clip
Check timestamp and participant ID associated
Verify consent stored in participant profile
Test consent export for auditing
Verify consent cannot be backdated or falsified

Step 3: Consent Revocation

Participant revokes consent mid-session
Verify immediate cessation of recording/photography
Test notification to support worker: "Recording stopped - consent revoked"
Verify existing recordings marked for review/deletion
Test partial consent (e.g., recording OK but photos not OK)

Step 4: Consent Expiry & Renewal

Configure consent expiry period (e.g., 12 months)
Advance system date to expiry
Verify consent re-requested before recording
Test renewal process simple and clear
Verify expired consent prevents data collection

Step 5: Guardian/Caregiver Consent

Test scenarios where participant cannot provide consent
Verify guardian consent properly documented
Test guardian contact info required
Verify guardian can revoke consent remotely (if feature enabled)

Step 6: Consent for Specific Uses

Test granular consent options:
- Recording for documentation only
- Photos for care planning
- Data sharing with healthcare providers
- Anonymous data for research (opt-in)
Verify each permission independently managed
Test use restrictions properly enforced

Step 7: NDIS Compliance Audit

Review all consent records for completeness
Verify audit trail shows:
- Who gave consent
- When consent given
- What was consented to
- Any revocations or changes
Test consent report generation for NDIS audits
Verify meets NDIS Practice Standards (Core Module)

Pass Criteria

✅ Consent obtained before any data collection
✅ Consent prompts clear and accessible
✅ Multiple response methods functional
✅ Verbal consent recorded with audio clip
✅ Consent revocation immediate and effective
✅ Expired consent blocks data collection
✅ Guardian consent properly documented
✅ Granular consent options functional
✅ Audit trail complete and tamper-proof
✅ Meets NDIS Practice Standards
✅ No consent bypasses or workarounds possible

Failure Modes to Monitor

System allows recording without consent
Consent revocation doesn't stop recording
Audit trail incomplete or falsifiable
Consent expiry not enforced
Guardian consent process unclear
Participant cannot easily understand consent prompts

TC-VAR-CL-003: Fall Detection for NDIS Participants

Priority: P1 - Critical
Test Type: Safety - Fall Prevention
Estimated Duration: 3 hours
Prerequisites: GF-CL with enhanced fall detection enabled

Objective

Validate that fall detection accurately identifies when a participant falls, minimizes false positives during normal activities, and triggers appropriate alerts to support workers.

Test Equipment

GF-CL device worn by participant
Safety mats and crash pads
Support worker with companion app
Test environment simulating home/community settings
Medical supervisor (for safety during controlled falls)

Test Procedure

Step 1: Controlled Fall Detection ⚠️ SAFETY CRITICAL: Requires medical supervision and safety equipment

Participant performs 10 controlled falls onto safety mats:
- Forward fall (3 repetitions)
- Backward fall (3 repetitions)
- Sideways fall left/right (2 repetitions each)
Verify fall detected within 1 second of impact
Record detection algorithm parameters:
- Acceleration threshold: |accel| > 2.8g
- Orientation change: > 45° from vertical
- Post-fall stillness: detected after 3 seconds
Verify 100% detection rate for genuine falls

Step 2: Alert Sequence Validation

After fall detected, verify 30-second countdown:
- HUD displays: "Fall detected. Are you OK? Respond within 30 seconds"
- Haptic vibration pulses
- Audio tone escalates
Test manual cancellation:
- Participant says "I'm OK" or "Cancel"
- Button press to cancel
If no response in 30 sec, verify automatic alert sent to support worker:
- SMS and push notification
- GPS location included
- Fall severity estimate (if available)
- Two-way audio call initiated

Step 3: False Positive Minimization

Participant performs 50 normal activities that may trigger false alarms:
- Sitting down quickly (10x)
- Lying down in bed (10x)
- Bending to tie shoes (5x)
- Getting into car (5x)
- Jumping/hopping (5x)
- Dropping object and catching it (5x)
- Tripping but recovering (5x)
- Dancing/exercising (5x)
Record false positive rate (target: ≤3%)
Verify system learns from false positives:
- Participant cancels false alarm
- System asks: "Was this a fall?" No → learn pattern
Test false positive rate improves over 1 week usage (should decrease 20-30%)

Step 4: Support Worker Alert & Response

Support worker receives fall alert on companion app
Verify alert includes:
- Participant name and photo
- GPS location with map
- Timestamp
- Fall severity estimate
- "Potential fall detected" status
Support worker initiates two-way audio
Measure call connection time (should be ≤5 seconds)
Test audio quality sufficient to assess participant status

Step 5: Multi-Participant Monitoring

Support worker responsible for 3-5 participants
Configure fall detection for all participants
Simulate fall for one participant
Verify support worker alerted with correct participant ID
Test alert prioritization if multiple falls occur
Verify support worker can manage alerts effectively

Step 6: Integration with Care Plans

Configure participant fall risk level (low/medium/high)
Verify alert behavior adjusts:
- High risk: Shorter timeout (15 sec), escalate faster
- Low risk: Standard timeout (30 sec)
Test fall incident automatically logged
Verify falls tracked over time for care plan reviews

Step 7: Privacy & NDIS Compliance

Verify fall detection can be disabled if participant doesn't consent
Test fall incident reports meet NDIS standards
Verify participant/guardian notified of fall alerts sent
Test data retention policy (falls stored 2 years per NDIS requirements)

Pass Criteria

✅ Fall detection sensitivity ≥95%
✅ Fall detection latency ≤1 second
✅ False positive rate ≤3%
✅ Alert sent within 30 seconds if no response
✅ Support worker alert includes GPS and severity
✅ Two-way audio connects ≤5 seconds
✅ System learns from false positives
✅ Multi-participant monitoring functional
✅ Care plan integration accurate
✅ NDIS compliance and privacy standards met

Failure Modes to Monitor

Fails to detect genuine falls
Excessive false positives cause alert fatigue
Alert delivery fails or delayed
GPS location inaccurate or missing
Two-way audio fails to connect
Participant unable to cancel false alarms easily

TC-VAR-CL-004: Incident Reporting & Flagging

Priority: P2 - High
Test Type: Software - Compliance
Estimated Duration: 2 hours
Prerequisites: GF-CL with incident reporting module enabled

Objective

Validate that the incident reporting system accurately detects and flags incidents during support sessions, generates compliant incident reports, and ensures timely escalation per NDIS requirements.

Test Equipment

GF-CL device
Incident reporting test scenarios (5 types)
NDIS incident reporting guidelines
Support worker testers (2)

Test Procedure

Step 1: Automatic Incident Detection

During recording, support worker mentions keywords indicating incidents:
- "injury"
- "accident"
- "medication error"
- "behavioral incident"
- "unauthorized restraint"
- "concern for safety"
Verify system automatically flags these for incident report
Test HUD notification: "Potential incident detected. Create report?"
Support worker can confirm or dismiss

Step 2: Manual Incident Reporting

Support worker initiates incident report: "Create incident report"
System prompts for incident details:
- Type: (injury/medication/behavior/abuse/neglect/other)
- Severity: (low/medium/high/critical)
- Participants involved
- Location and time
- Description of incident
- Actions taken
- Notifications made
Verify voice-based form filling
Test report generation time (≤5 minutes)

Step 3: Incident Classification & Severity

Test AI classification of incident type based on description
Verify severity automatically assessed:
- Critical: Serious injury, abuse, death → immediate escalation
- High: Injury requiring medical attention, medication error
- Medium: Minor injury, behavioral incident managed
- Low: Near-miss, environmental hazard identified
Test manual override of classification

Step 4: Mandatory Reporting Triggers

Test reportable incidents per NDIS requirements:
- Death of participant
- Serious injury
- Abuse or neglect
- Unauthorized use of restrictive practices
- Sexual or physical assault
Verify system flags these as "Reportable Incident"
Test automatic escalation to NDIS Commission (if configured)
Verify 24-hour reporting deadline tracked

Step 5: Incident Report Generation

System generates structured incident report
Verify report includes required NDIS sections:
- Incident classification
- People involved (participant, staff, witnesses)
- Date, time, location
- Detailed description
- Immediate actions taken
- Injuries or harm caused
- Witnesses and statements
- Notifications made (family, guardian, NDIS Commission)
- Follow-up required
- Support worker signature
Export as PDF with proper formatting

Step 6: Escalation & Notifications

For high/critical incidents, verify automatic notifications:
- Service provider management
- Participant's guardian/family
- Participant's support coordinator
- NDIS Commission (for reportable incidents)
Test notification delivery methods: email, SMS, app push
Verify escalation within required timeframes:
- Critical: Immediate (≤15 minutes)
- High: Within 2 hours
- Medium: Within 24 hours

Step 7: Incident Tracking & Follow-Up

Test incident register/log maintenance
Verify incidents tracked with status:
- Reported
- Under investigation
- Actions taken
- Closed
Test follow-up reminders for support worker
Verify trends and analytics available for service provider:
- Incident frequency
- Types of incidents
- High-risk participants or locations

Pass Criteria

✅ Automatic detection flags 100% of critical incidents
✅ Manual incident report creation ≤5 minutes
✅ Incident classification ≥90% accurate
✅ Reportable incidents flagged 100%
✅ Escalation triggers within required timeframes
✅ Report includes all required NDIS sections
✅ Notifications delivered reliably
✅ Incident register tracking functional
✅ Meets NDIS incident reporting requirements
✅ Support worker satisfaction ≥4.0/5.0

Failure Modes to Monitor

Critical incidents not detected or flagged
Escalation delays or fails
Required report sections missing
Notifications not delivered
Incident data lost or corrupted
24-hour reporting deadline not tracked

TC-VAR-CL-005: Care Plan Goal Tracking

Priority: P2 - High
Test Type: Software - Care Management
Estimated Duration: 3 hours
Prerequisites: GF-CL with care plan module configured

Objective

Validate that the care plan goal tracking system accurately links support activities to participant goals, tracks progress over time, and generates progress reports for NDIS plan reviews.

Test Equipment

GF-CL device
Sample NDIS care plan with 3-5 goals
3 support sessions to track
Support worker and participant

Test Procedure

Step 1: Care Plan Configuration

Input participant's NDIS care plan goals:
- Goal 1: Increase community participation (2 activities per week)
- Goal 2: Improve cooking skills (prepare 1 meal independently per week)
- Goal 3: Build social connections (attend 1 social group per fortnight)
- Goal 4: Manage health appointments (attend all scheduled appointments)
- Goal 5: Develop budgeting skills (track expenses daily)
Verify goals stored securely
Test goal editing and updates
Verify goals sync across devices (if multi-device support)

Step 2: Activity-to-Goal Linking

During support session, support worker links activities to goals:
- Voice command: "This addresses goal 2" while documenting cooking activity
- Manual selection from goal list
- AI auto-suggestion based on activity description
Verify link created accurately
Test multiple goals linked to single activity (if appropriate)
Verify unlinked activities flagged for review

Step 3: Progress Tracking & Measurement

Over 3 support sessions, document activities linked to goals
Verify system tracks:
- Goal 1: Community outings count (target: 2/week)
- Goal 2: Meals prepared count (target: 1/week)
- Goal 3: Social groups attended (target: 1/fortnight)
Test progress visualization:
- Progress bars or percentage complete
- Charts showing trend over time
- Color coding: green = on track, yellow = at risk, red = not achieved
Verify progress calculations accurate

Step 4: Participant Engagement & Response

Document participant's response to activities:
- Engagement level: (low/medium/high)
- Enjoyment rating: (1-5 scale)
- Skill development observed
- Challenges encountered
Test AI analysis of engagement patterns:
- Identifies activities participant enjoys most
- Flags activities with low engagement for review
Verify recommendations for goal adjustment if appropriate

Step 5: Progress Report Generation

Generate progress report for NDIS plan review
Verify report includes:
- All goals with current status
- Activities undertaken per goal
- Progress percentage toward each goal
- Participant engagement and response
- Barriers and challenges encountered
- Recommendations for ongoing support
- Support worker observations
Test report formatting suitable for NDIS submission
Export as PDF with proper structure

Step 6: Goal Review & Adjustment

Test goal modification mid-cycle:
- Increase/decrease frequency targets
- Add new goals
- Archive completed goals
Verify historical progress preserved when goals change
Test impact analysis: "If we change goal 2 to 2 meals/week, will participant be on track?"

Step 7: Multi-Support Worker Coordination

Configure multiple support workers for one participant
Verify all workers can see same goals and progress
Test activity contributions tracked per worker
Verify no data conflicts or overwrites
Test collaborative progress tracking

Pass Criteria

✅ Goals configured correctly from NDIS plan
✅ Activity linking ≥95% accurate
✅ Progress tracking calculations correct
✅ Progress visualization clear and helpful
✅ Engagement tracking functional
✅ Progress report includes all required sections
✅ Report suitable for NDIS plan review
✅ Goal adjustments preserved in history
✅ Multi-worker coordination functional
✅ Support worker satisfaction ≥4.5/5.0

Failure Modes to Monitor

Activities not linked to appropriate goals
Progress calculations incorrect
Engagement patterns not detected
Progress report incomplete or inaccurate
Goal changes cause data loss
Multi-worker conflicts or overwrites

3. GF-NF: Nick's Fitness Variant

Target User: Athletes, fitness enthusiasts, active lifestyle users

TC-VAR-NF-001: Advanced Health Sensor Accuracy

Priority: P1 - Critical
Test Type: Hardware - Biometric Sensors
Estimated Duration: 4 hours
Prerequisites: GF-NF with MAX30102 (HR/SpO2) and MLX90614 (temp) sensors

Objective

Validate that advanced health sensors provide accurate real-time measurements of heart rate, SpO2, and temperature compared to medical-grade reference devices.

Test Equipment

GF-NF device
Medical-grade pulse oximeter (reference device)
Medical-grade forehead thermometer (reference device)
ECG chest strap heart rate monitor (reference device)
Exercise equipment: treadmill or stationary bike
3 test subjects with varying fitness levels
Data logging system

Test Procedure

Step 1: Baseline Measurements (Resting)

Subject sits quietly for 5 minutes
Simultaneously measure with GF-NF and reference devices:
- Heart rate (bpm)
- SpO2 (%)
- Forehead temperature (°C)
Record 10 readings at 30-second intervals
Calculate accuracy metrics:
- Mean absolute error (MAE)
- Root mean square error (RMSE)
- Bland-Altman analysis

Target Accuracy:

Heart rate: ±2 bpm or ±5% (whichever greater)
SpO2: ±2% absolute
Temperature: ±0.3°C

Step 2: Dynamic Exercise Testing

Subject performs graduated exercise protocol:
- Warm-up: 5 min at 50% max HR
- Moderate: 5 min at 70% max HR
- Intense: 3 min at 85% max HR
- Cool-down: 5 min gradual decrease
Continuously record all sensors (GF-NF and reference)
Measure update latency: how quickly GF-NF responds to HR changes
Verify GF-NF tracks rapid HR changes during transitions

Target Performance:

HR tracking latency: ≤3 seconds
Maintains accuracy during exercise (±5 bpm)
No sensor dropout during movement
Updates displayed every 1-2 seconds

Step 3: Motion Artifact Testing

Subject performs activities with high motion:
- Running (outdoor or treadmill)
- Cycling with upper body movement
- Resistance training (arm movements)
- High-intensity interval training
Verify sensors maintain reasonable accuracy
Identify motion conditions causing sensor degradation

Acceptable Degradation:

HR accuracy may decrease to ±10 bpm during high motion
System should flag "low confidence" readings
Must recover quickly when motion stabilizes

Step 4: SpO2 Accuracy Validation

Test SpO2 across normal physiological range:
- Healthy baseline: 96-100%
- Mild hypoxemia simulation: 92-95% (breath-holding if safe)
- Medical supervision required for < 92%
Compare GF-NF vs medical pulse oximeter
Test in varied conditions:
- Good perfusion (warm hands)
- Poor perfusion (cold hands, after exercise)
- Different skin tones (sensor performance may vary)

Target Accuracy:

SpO2 ≥90%: ±2% absolute
SpO2 < 90%: ±3% absolute
Flag unreliable readings (poor signal quality)

Step 5: Temperature Measurement

Baseline forehead temp measurement
Test temperature changes:
- Post-exercise (expect 0.5-1.0°C increase)
- Cold exposure (ice pack on forehead briefly)
- Ambient temperature variations
Verify temperature sensor detects trends accurately
Test fever detection threshold (≥37.5°C alert)

Target Accuracy:

Core temperature estimation: ±0.3°C
Trend detection: identifies increasing/decreasing temp
Fever alert triggers appropriately

Step 6: Sensor Placement & Fit Testing

Test sensor accuracy with different frame fits:
- Snug fit (optimal)
- Loose fit (glasses sliding)
- Misaligned sensors
Verify system detects poor sensor contact
Test notification: "Adjust glasses for better sensor readings"
Measure how fit affects accuracy

Step 7: Multi-Sensor Fusion & Validation

Test derived metrics calculated from sensor fusion:
- VO2 max estimation (requires multi-day data)
- Recovery heart rate (HR decrease 1 min post-exercise)
- Stress level estimation (HRV if available)
- Fatigue score
Validate derived metrics against known physiological principles
Test that unreliable sensor data doesn't corrupt derived metrics

Step 8: Long-Duration Accuracy

Subject wears GF-NF for 2-hour workout session
Continuous monitoring with reference devices
Verify sensor accuracy doesn't degrade over time
Check for sensor drift or calibration issues
Verify battery life sufficient for session (≥6 hours active use)

Pass Criteria

✅ Heart rate resting: MAE ≤2 bpm, ≥95% readings within ±5 bpm
✅ Heart rate exercise: MAE ≤5 bpm, ≥90% readings within ±10 bpm
✅ HR tracking latency ≤3 seconds
✅ SpO2 accuracy: ≥95% readings within ±2%
✅ Temperature accuracy: ≥95% readings within ±0.3°C
✅ Motion artifact handling acceptable (degradation < 15%)
✅ System flags low-confidence readings
✅ Sensor placement issues detected
✅ Derived metrics physiologically reasonable
✅ No accuracy degradation over 2-hour use

Failure Modes to Monitor

HR readings wildly inaccurate or erratic
SpO2 readings unrealistic (e.g., < 85% in healthy subject)
Temperature sensor non-responsive or incorrect
Sensors fail during exercise
Motion artifacts cause excessive errors
Battery drains too quickly with continuous sensing
Sensor placement issues not detected

TC-VAR-NF-002: Fatigue Detection & Recovery Recommendations

Priority: P1 - Critical
Test Type: Software - AI Health Analysis
Estimated Duration: 5 hours + overnight testing
Prerequisites: GF-NF with fatigue detection AI and multi-day data

Objective

Validate that the fatigue detection system accurately assesses user tiredness, predicts performance degradation, and provides actionable recovery recommendations to prevent overtraining.

Test Equipment

GF-NF device
3 test subjects (athletes)
Multi-day testing period (3-5 days)
Standardized fitness tests (performance benchmarks)
Subjective fatigue assessment (questionnaires)
Sleep tracking reference device (if available)

Test Procedure

Step 1: Baseline Data Collection (Day 1-2)

Subjects wear GF-NF continuously for 48 hours
Collect baseline biometric data:
- Resting heart rate (morning)
- Heart rate variability (HRV)
- Sleep duration and quality (if tracked)
- Daily activity levels
- Workout intensity and volume
Subjects complete daily self-assessment:
- Fatigue level (1-10 scale)
- Mood/motivation
- Muscle soreness
- Sleep quality rating
Verify system establishes baseline "normal" for each subject

Step 2: Fatigue Induction (Day 3)

Subjects perform intense workout exceeding normal volume:
- 150% of typical workout duration
- Higher intensity than usual
- Insufficient recovery time between sets
System should detect fatigue indicators:
- Elevated resting HR (+10% from baseline)
- Decreased HRV (-20% from baseline)
- Longer time to HR recovery post-exercise
- Increased perceived exertion for same workload
Verify fatigue score increases during day

Step 3: Fatigue Assessment Accuracy

Compare GF-NF fatigue score to self-reported fatigue
Calculate correlation (target: r ≥ 0.70)
Verify fatigue categories aligned:
- Fresh (score 1-3): Ready for intense training
- Moderate (4-6): Normal training OK, avoid overload
- Fatigued (7-8): Light exercise only, prioritize recovery
- Exhausted (9-10): Rest day required, risk of injury
Test sensitivity: does system detect increasing fatigue?
Test specificity: does system not flag false fatigue?

Step 4: Performance Prediction Validation

Subjects perform standardized fitness test when GF-NF indicates:
- Fresh state (fatigue score 2)
- Moderately fatigued (score 5)
- Highly fatigued (score 8)
Compare actual performance to prediction:
- Example: Fresh = 10 reps max, Moderate = 8 reps, Fatigued = 6 reps
Verify GF-NF predictions within ±1 category
Test warning system: "You may underperform today. Consider lighter training."

Step 5: Recovery Recommendations

When fatigue detected, verify system provides actionable recommendations:
- "Take rest day or active recovery only"
- "Get 8+ hours sleep tonight"
- "Hydrate and refuel properly"
- "Consider stretching or massage"
- "Avoid high-intensity training for 24-48 hours"
Test personalization: recommendations tailored to user's training goals
Verify recommendations aligned with sports science best practices

Step 6: Recovery Tracking (Day 4-5)

Subjects follow recovery recommendations
Monitor fatigue score improvement over 24-48 hours
Verify system detects recovery:
- Resting HR returns to baseline
- HRV improves
- Fatigue score decreases
Test "ready to train" notification when fully recovered

Step 7: Overtraining Prevention

Simulate overtraining pattern:
- Multiple consecutive days of intense training
- Insufficient recovery
- Declining performance despite effort
Verify early warning system:
- Alert after 2-3 days of excessive training without recovery
- Warning: "Risk of overtraining. Rest day recommended."
- Escalating urgency if warnings ignored
Test integration with training calendar (if available):
- Suggests rest days proactively
- Adjusts training plan based on fatigue

Step 8: Sleep Quality Integration

Test correlation between sleep and fatigue:
- Poor sleep (≤6 hours) → higher fatigue score next day
- Good sleep (≥8 hours) → improved recovery
Verify sleep recommendations:
- "Prioritize 8+ hours sleep for recovery"
- Bedtime reminders if fatigue high
Test sleep tracking accuracy (if GF-NF has sleep mode):
- Total sleep time ±15 minutes vs reference device
- Sleep quality assessment reasonable

Step 9: Long-Term Trend Analysis

After 3-5 days of data, generate fatigue trend report
Verify report shows:
- Fatigue patterns over time (chart/graph)
- Correlation between training load and fatigue
- Recovery patterns
- Training volume recommendations
Test predictive analytics:
- "Based on your training, you'll need a rest day in 2 days"
Verify insights actionable and understandable

Pass Criteria

✅ Fatigue score correlates with self-reported fatigue (r ≥ 0.70)
✅ Baseline established within 48 hours
✅ Fatigue detection sensitivity ≥85% (detects actual fatigue)
✅ Fatigue detection specificity ≥90% (no false positives)
✅ Performance predictions within ±1 category
✅ Recovery recommendations evidence-based and personalized
✅ Recovery detection accurate (fatigue score decreases appropriately)
✅ Overtraining warning system functional
✅ Sleep quality integration logical
✅ Long-term trends provide actionable insights
✅ User satisfaction ≥4.3/5.0

Failure Modes to Monitor

Fatigue score doesn't match actual fatigue state
System fails to detect overtraining patterns
Recovery recommendations generic or unhelpful
False positives cause unnecessary rest days
Sleep integration inaccurate or misleading
Trend analysis doesn't provide useful insights

TC-VAR-NF-003: Real-Time Performance Tracking

Priority: P2 - High
Test Type: Software - Fitness Analytics
Estimated Duration: 3 hours
Prerequisites: GF-NF with performance tracking module enabled

Objective

Validate that real-time performance tracking accurately measures workout metrics, displays live feedback during exercise, and provides post-workout analysis to improve training effectiveness.

Test Equipment

GF-NF device
Exercise equipment with built-in metrics (treadmill, bike, rower)
Stopwatch for lap timing
2-3 test subjects (athletes)
Standardized workout protocol

Test Procedure

Step 1: Workout Detection & Auto-Start

Subject begins exercise (running, cycling, strength training)
Verify GF-NF automatically detects workout start:
- HR increases above resting + 20 bpm
- Motion patterns consistent with exercise
Test HUD notification: "Workout detected. Start tracking?"
Verify manual workout start option available
Test workout type auto-classification:
- Running (cadence + HR pattern)
- Cycling (motion pattern)
- Strength training (interval pattern)

Step 2: Real-Time Metrics Display

During workout, verify HUD displays:
- Current heart rate (bpm)
- HR zone (Z1-Z5 color-coded)
- Elapsed time
- Calories burned (estimate)
- Pace/cadence (if applicable)
Test metric update frequency (every 1-2 seconds)
Verify display is readable during exercise (no blur, proper contrast)
Test customizable metric display (user chooses what to show)

Step 3: Heart Rate Zone Training

Pre-configure HR zones based on max HR (220 - age):
- Z1 (50-60%): Warm-up/recovery
- Z2 (60-70%): Fat burn/aerobic base
- Z3 (70-80%): Aerobic endurance
- Z4 (80-90%): Threshold training
- Z5 (90-100%): Max effort
Subject performs interval workout:
- 5 min Z2 → 3 min Z4 → 2 min Z2 → 3 min Z4 → 5 min Z2
Verify system alerts at zone transitions:
- Audio cue: "Entering Zone 4"
- HUD color changes
- Haptic pulse (optional)
Test time-in-zone tracking (displays during workout)
Verify target zone guidance: "Increase effort to reach Zone 4"

Step 4: Pace & Cadence Tracking

For running, verify stride rate (cadence) measurement:
- Using IMU motion patterns
- Target accuracy: ±5 steps/min vs manual count
Test pace calculation (min/km or min/mile):
- Compare to treadmill or GPS watch
- Target accuracy: ±5% or ±10 sec/km
Verify real-time pace feedback:
- "Current pace: 5:30/km"
- "Target pace: 5:00/km - increase effort"

Step 5: Interval Training Mode

Configure interval workout:
- 8 rounds: 30 sec work, 30 sec rest
System provides audio/visual cues:
- "Work" / "Rest" audio prompts
- Countdown timer on HUD
- Haptic pulse at transitions
Verify timing accuracy (±0.5 seconds)
Test customizable intervals:
- Tabata: 20 sec work, 10 sec rest, 8 rounds
- Custom: any work/rest duration and round count

Step 6: Post-Workout Summary

Workout completes (auto-detect or manual stop)
Verify summary displayed:
- Total duration
- Average HR and peak HR
- Time in each HR zone (pie chart or bar graph)
- Calories burned
- Distance (if tracked)
- Average pace/cadence
- Personal records (if achieved)
Test summary export: PDF or share to apps (Strava, Apple Health, Google Fit)

Step 7: Workout History & Trends

View workout history (past 7 days, 30 days)
Verify trends displayed:
- Total workout time/week
- Average HR trends
- Performance improvements
- Volume progression
Test filtering by workout type
Verify weekly/monthly summary reports

Step 8: Integration with Training Goals

Set training goal: "Run 5K in < 25 minutes"
Verify system tracks progress toward goal
Test goal-specific workouts recommended:
- Speed work for pace improvement
- Long runs for endurance
- Recovery runs for adaptation
Verify goal achievement celebrated with notification

Step 9: Accuracy Cross-Validation

Compare GF-NF metrics to exercise equipment displays:
- Treadmill: speed, distance, calories
- Bike: power, cadence, distance
- Rower: pace, stroke rate
Calculate accuracy for each metric
Acceptable discrepancies:
- Distance: ±5%
- Calories: ±15% (estimates vary widely)
- Pace: ±5%

Pass Criteria

✅ Workout auto-detection ≥95% accuracy
✅ Workout type classification ≥85% accurate
✅ Real-time metrics update every 1-2 seconds
✅ HR zone detection accurate (±2 bpm thresholds)
✅ Time-in-zone tracking ≥98% accurate
✅ Cadence accuracy ±5 steps/min
✅ Pace accuracy ±5% or ±10 sec/km
✅ Interval timing ±0.5 seconds
✅ Post-workout summary complete and accurate
✅ Workout history and trends functional
✅ Training goal integration logical
✅ User satisfaction ≥4.5/5.0

Failure Modes to Monitor

Workout detection fails or delayed
Metrics inaccurate or erratic
HR zone thresholds miscalculated
Interval timer drift or skip transitions
Post-workout summary missing data
Export to fitness apps fails

TC-VAR-NF-004: UV Exposure & Outdoor Safety

Priority: P3 - Medium
Test Type: Hardware/Software - Environmental Sensing
Estimated Duration: 2 hours (requires sunlight)
Prerequisites: GF-NF with UV sensor and outdoor safety features

Objective

Validate that UV exposure monitoring accurately measures UV index, tracks cumulative exposure, and provides timely sunburn prevention alerts during outdoor exercise.

Test Equipment

GF-NF device
Reference UV meter (standalone device)
Outdoor testing location with direct sunlight
Test subject (fair to medium skin type)
Sun protection: sunscreen, hat

Test Procedure

Step 1: UV Index Measurement Accuracy

In full sunlight, measure UV index with GF-NF and reference meter
Test at different times of day:
- Morning (9-10 AM): typically UV 3-5
- Midday (12-2 PM): typically UV 7-10+
- Afternoon (4-5 PM): typically UV 2-4
Compare GF-NF reading to reference
Target accuracy: ±1 UV index unit
Test in various conditions:
- Clear sky (highest UV)
- Partly cloudy (variable UV)
- Shade (reduced UV)

Step 2: Cumulative UV Exposure Tracking

Subject wears GF-NF outdoors for 1 hour
System tracks cumulative UV exposure:
- Measured in standard erythema dose (SED) or minimal erythema dose (MED)
- Accounts for duration and UV intensity
Verify exposure calculation:
- Example: 30 min at UV 8 = 2 SED
Test display: "UV exposure today: 2.5 SED"
Compare to reference calculation

Step 3: Skin Type Personalization

Configure user skin type (Fitzpatrick scale I-VI):
- Type I: Very fair, always burns
- Type II: Fair, usually burns
- Type III: Medium, sometimes burns
- Type IV: Olive, rarely burns
- Type V-VI: Dark, very rarely burns
Verify sunburn risk threshold adjusts:
- Type I: Alert at 1 SED (~20 min at UV 8)
- Type III: Alert at 2-3 SED (~40 min at UV 8)
- Type V: Alert at 4+ SED ( > 1 hour at UV 8)

Step 4: Sunburn Prevention Alerts

Subject exercises outdoors without sunscreen
Monitor UV exposure accumulation
Verify alert when approaching sunburn threshold:
- "UV exposure high. Apply sunscreen soon."
- "You've reached safe sun limit. Seek shade."
Test alert timing (should warn before sunburn occurs)
Verify alerts escalate with urgency

Step 5: Sunscreen Application Tracking

User indicates sunscreen applied: "Applied sunscreen"
System adjusts sunburn threshold:
- SPF 30 → 30× longer safe exposure
- SPF 50 → 50× longer safe exposure
Verify reapplication reminders:
- "Reapply sunscreen after 2 hours"
- "Swimming detected - reapply sunscreen"
Test manual SPF entry (15, 30, 50+)

Step 6: Outdoor Activity Recommendations

System provides activity timing guidance:
- "UV will be highest 12-2 PM. Plan exercise before 11 AM."
- "UV index safe for outdoor run (UV 3)"
Verify recommendations personalized to skin type
Test integration with workout planning:
- Suggests indoor workout during peak UV hours
- Recommends shaded routes for runs

Step 7: Historical UV Exposure Trends

View UV exposure history (past week, month)
Verify trends displayed:
- Total UV exposure per day
- Cumulative monthly exposure
- Days exceeding safe limits
Test UV dose visualization (chart or graph)
Verify long-term skin health insights:
- "You're averaging safe UV exposure"
- "Consider more sun protection - 3 days exceeded limit last week"

Step 8: Integration with Other Health Metrics

Test correlation between outdoor exercise and:
- Vitamin D synthesis estimate (positive effect of moderate sun)
- Heat exposure warnings (UV + temperature)
- Hydration reminders (hot sun increases fluid needs)
Verify holistic outdoor safety guidance

Pass Criteria

✅ UV index accuracy ±1 unit vs reference meter
✅ Cumulative exposure calculation correct (±10%)
✅ Skin type personalization functional
✅ Sunburn alerts timely (before threshold reached)
✅ Sunscreen application adjusts threshold correctly
✅ Reapplication reminders at appropriate intervals
✅ Activity recommendations logical and helpful
✅ Historical trends displayed accurately
✅ Integration with health metrics reasonable
✅ User finds feature helpful (≥4.0/5.0 rating)

Failure Modes to Monitor

UV sensor inaccurate or non-responsive
Cumulative exposure calculation wrong
Alerts too late (sunburn occurs)
Sunscreen adjustment inappropriate
Skin type not affecting thresholds
Excessive alerts cause annoyance

TC-VAR-NF-005: Guided Training & Coaching

Priority: P3 - Medium
Test Type: Software - AI Training Coach
Estimated Duration: 4 hours
Prerequisites: GF-NF with AI coaching module enabled

Objective

Validate that the AI training coach provides effective real-time coaching cues, personalized workout guidance, and motivational feedback to enhance training quality and adherence.

Test Equipment

GF-NF device
3 test subjects (varying fitness levels)
Exercise equipment (treadmill, weights, mat for bodyweight)
Standardized workout protocols
Coach effectiveness questionnaire

Test Procedure

Step 1: Personalized Workout Generation

User configures fitness goals:
- Goal: Improve 5K running time
- Current fitness: Runs 3x per week, 5K in 28 minutes
- Available time: 45 min per session
AI generates personalized workout plan:
- 3-4 runs per week
- Mix of easy runs, tempo runs, intervals
- Progressive overload over 4 weeks
Verify workout plan reasonable and achievable
Test plan adjusts based on user feedback:
- "That was too hard" → reduce intensity next session
- "That felt easy" → increase difficulty next session

Step 2: Real-Time Form Coaching (if camera available)

User performs bodyweight exercises:
- Push-ups, squats, lunges, planks
AI provides form feedback:
- "Lower your hips in plank position"
- "Go deeper in squat - knees to 90°"
- "Elbows at 45° for push-ups"
Verify feedback accurate and helpful
Test feedback frequency (not overwhelming, not too sparse)

Step 3: Pacing Guidance (Running/Cycling)

User performs interval workout:
- 5 min warm-up → 4×(3 min hard, 2 min easy) → 5 min cool-down
AI provides real-time pacing cues:
- "Speed up - target pace is 5:00/km"
- "Slow down - you're going too fast for this interval"
- "Hold this pace for 2 more minutes"
Verify cues timely and appropriate
Test adaptive pacing (adjusts if user can't maintain target)

Step 4: Motivational Feedback

During difficult workout segments, verify motivational cues:
- "You've got this - 30 seconds left!"
- "Strong effort! Keep it up!"
- "Last interval - finish strong!"
Test personalization:
- Encouraging tone for beginners
- Competitive tone for advanced athletes
- Calm analytical tone if user prefers
Verify motivational cues don't annoy user

Step 5: Recovery & Rest Day Guidance

After hard workout, verify recovery recommendations:
- "Great session! Take it easy tomorrow."
- "Active recovery recommended - light jog or yoga"
Test integration with fatigue detection:
- If highly fatigued, coach suggests rest day
- If fresh, coach suggests challenging workout
Verify rest day importance communicated

Step 6: Progress Celebration & Milestones

User achieves personal record or milestone:
- Fastest 5K
- Longest run
- Most workouts in a week
Verify celebration notification:
- "New personal record! You ran 5K in 26:30!"
- Badge or achievement unlocked
Test motivational impact of celebrations

Step 7: Technique Cues (Strength Training)

User performs resistance exercises
AI provides technique reminders:
- "Control the descent - 2 seconds down"
- "Full range of motion - chest to floor"
- "Breathe - exhale on exertion"
Verify cues aligned with proper technique
Test rep counting (if motion tracking available):
- "Rep 8 of 10 - 2 more!"

Step 8: Adaptive Difficulty Adjustment

Over multiple workouts, verify AI adapts difficulty:
- If user consistently exceeds targets → increase difficulty
- If user frequently fails to complete → reduce difficulty
Test progressive overload:
- Gradual increase in volume or intensity
- Suitable for user's adaptation rate
Verify no sudden jumps causing injury risk

Step 9: Coach Effectiveness Evaluation

After 3-4 coached workouts, subjects complete questionnaire:
- Coaching cues helpful? (1-5 scale)
- Workouts appropriate difficulty? (1-5 scale)
- Motivation increased? (1-5 scale)
- Would use coach feature regularly? (yes/no)
Calculate average ratings
Collect qualitative feedback on improvements

Pass Criteria

✅ Personalized workout plans reasonable and achievable
✅ Real-time coaching cues accurate and timely
✅ Pacing guidance helps maintain target effort
✅ Motivational feedback perceived as helpful (≥4.0/5.0)
✅ Recovery guidance appropriate and science-based
✅ Progress celebrations motivating
✅ Technique cues aligned with proper form
✅ Adaptive difficulty logical and safe
✅ Coach feature improves workout quality (≥70% of users agree)
✅ User satisfaction ≥4.3/5.0
✅ ≥60% of users would use feature regularly

Failure Modes to Monitor

Workout plans too difficult or too easy
Coaching cues inaccurate or misleading
Pacing guidance causes overexertion
Motivational feedback annoying
Recovery guidance ignored or unclear
Adaptive difficulty changes too aggressively
Users disable coach feature due to annoyance

4. GF-TX: TradeForce Pro (Industrial) Variant

Target User: Trades, industrial workers, field service technicians, construction workers

TC-VAR-TX-001: IP65 Protection & Durability

Priority: P1 - Critical
Test Type: Hardware - Environmental Resistance
Estimated Duration: 8 hours
Prerequisites: GF-TX device with IP65 certification claim

Objective

Validate that the GF-TX device meets IP65 ingress protection standards, withstands dust exposure, low-pressure water jets, and typical industrial abuse without compromising functionality.

Test Equipment

GF-TX device
IP testing chamber (dust and water)
Dust: talcum powder or fine sand
Water spray nozzle (6.3mm, 12.5 L/min flow rate)
Drop test apparatus
Vibration testing equipment
Temperature chamber
Functionality test equipment post-abuse

Test Procedure

Step 1: Dust Protection Testing (IP6X - Dust Tight) ⚠️ Requires certified IP testing lab for official certification

Place GF-TX in vacuum dust chamber
Create negative pressure inside chamber
Introduce talcum powder or fine dust particles
Maintain exposure for 8 hours at -20 mbar pressure
Remove device and inspect interior:
- No dust penetration visible
- Internal components clean
- Seals intact
Test full functionality after dust exposure:
- Power on
- Sensors operational
- Audio clear
- Display functional
- Charging works
- No dust inside battery compartment

Step 2: Water Protection Testing (IPX5 - Water Jets) ⚠️ Requires certified IP testing lab for official certification

Configure water jet test apparatus:
- 6.3mm diameter nozzle
- 12.5 liters per minute flow rate
- 2.5-3 meters distance
- Water pressure: 30 kPa
Spray GF-TX from all angles for 3 minutes each:
- Front
- Back
- Left side
- Right side
- Top
- Bottom
Total exposure time: minimum 15 minutes
Verify no water ingress:
- Shake device and listen for water inside
- Visual inspection of sealed areas
- Weigh before/after (no significant weight increase)
Dry exterior and test full functionality:
- Power on successfully
- All sensors working
- Audio quality unaffected
- Display clear
- USB-C charging works
- No condensation inside display

Step 3: Combined Dust & Water Exposure

Perform sequential testing:
- Dust test → functionality check → water test → functionality check
Verify device survives both conditions
Test represents worst-case scenario (dusty construction site + rain)

Step 4: Drop & Impact Testing

Perform controlled drops from 1.2 meters:
- 6 drops (one on each face/edge/corner) as per MIL-STD-810G
- Onto concrete surface
- Device in powered-off state
Visual inspection after each drop:
- Check for cracks, breaks, or separation
- Verify seals intact
- Check lens integrity
Functionality test after all drops:
- Powers on successfully
- No rattling or loose components
- All features operational

Step 5: Vibration & Shock Testing

Mount GF-TX to vibration table
Apply vibration profile simulating:
- Tool vibration: 10-150 Hz, 1 hour
- Vehicle transport: 5-500 Hz, 2 hours
- Peak acceleration: 5g
Verify no mechanical failures:
- Connections remain secure
- Battery stays in place
- Sensors remain calibrated

Step 6: Temperature Cycling

Expose GF-TX to temperature extremes:
- Cold: -10°C for 2 hours
- Hot: +50°C for 2 hours
- Rapid transition (thermal shock)
Perform 3 cycles
Verify functionality at each temperature:
- Battery charges properly
- Display remains responsive
- Sensors accurate

Step 7: Reinforced Areas Validation

Inspect reinforced components:
- USB-C port cover (tight seal)
- Battery compartment gasket
- Frame sealing points
- Display-to-frame seal
Verify no gaps or incomplete sealing
Test repeated opening/closing of sealed compartments:
- 50 cycles
- Seals remain effective

Step 8: Field Simulation Testing

Simulate typical work day conditions:
- Morning: Device exposed to dust (sawdust, drywall dust)
- Midday: Brief rain exposure
- Afternoon: Dropped from tool belt height
- Evening: Charging with dusty hands (port contamination)
Verify device remains functional throughout
Test cleaning procedures:
- Can be wiped clean
- Ports can be blown clear of dust
- No damage from routine cleaning

Pass Criteria

✅ No dust penetration after 8-hour exposure (IP6X)
✅ No water ingress after 15-min jet spray (IPX5)
✅ Full functionality maintained after dust exposure
✅ Full functionality maintained after water exposure
✅ Survives 6× drops from 1.2m with no critical failures
✅ No mechanical failures after vibration testing
✅ Operates correctly across -10°C to +50°C
✅ Seals remain effective after 50 open/close cycles
✅ Survives full-day field simulation
✅ Meets IP65 standard (official certification required)

Failure Modes to Monitor

Dust penetration into sealed compartments
Water ingress causing electrical failures
Cracked display or housing from drops
Seal degradation from repeated access
Component failures from vibration
Thermal damage to battery or electronics
Port covers fail to seal properly

TC-VAR-TX-002: OH&S Reminders & Safety Compliance

Priority: P1 - Critical
Test Type: Software - Occupational Safety
Estimated Duration: 3 hours
Prerequisites: GF-TX with OH&S safety module enabled

Objective

Validate that the OH&S (Occupational Health & Safety) reminder system provides timely safety prompts, enforces compliance with safety procedures, and adapts to different work environments and hazards.

Test Equipment

GF-TX device
Simulated work environments (workshop, construction site, confined space)
Safety equipment (PPE: helmet, gloves, safety glasses)
OH&S checklists for various trades
Test workers from target industries (2-3)

Test Procedure

Step 1: PPE Compliance Checking

Configure required PPE for work environment:
- Construction site: Hard hat, safety boots, high-vis vest, safety glasses, gloves
- Workshop: Safety glasses, hearing protection, closed-toe shoes
- Confined space: Full PPE + gas monitor + harness
Worker starts work without PPE
Verify system prompts: "Put on safety glasses before starting work"
Test visual + audio + haptic alert escalation
Worker dons PPE
Verify system acknowledges: "PPE confirmed. Safe to proceed."

Optional Camera-Based PPE Detection:

If GF-TX has computer vision, test automatic PPE detection
System visually confirms hard hat, vest, gloves in view
Reduces reliance on manual confirmation

Step 2: Pre-Start Safety Checklist

Configure daily pre-start checklist:
- "Inspect work area for hazards"
- "Verify tools in good condition"
- "Check emergency exits are clear"
- "Review SOP for today's tasks"
Worker starts shift
Verify system prompts checklist
Test voice responses: "Checklist item 1, complete"
Verify checklist must be completed before proceeding
System logs checklist completion with timestamp

Step 3: Time-Based Safety Reminders

Configure periodic safety reminders:
- Hearing protection: "Remove hearing protection and check ears every 2 hours"
- Hydration: "Drink water - 30°C+ temperature detected"
- Break reminders: "Take 15-minute break after 4 hours work"
- Eye wash: "If chemical splash, rinse eyes for 15 minutes"
Verify reminders triggered at appropriate intervals
Test snooze/dismiss functionality
Verify repeated reminders if not acknowledged

Step 4: Hazard-Specific Alerts

Test environmental hazard detection and alerts:
- High noise: "Noise level > 85 dB. Hearing protection required."
- High temperature: "Temperature > 35°C. Increase fluid intake."
- Low light: "Lighting insufficient. Use task light."
- Poor air quality: "VOC detected. Ensure ventilation adequate." (if BME688 sensor)
Verify alerts trigger when thresholds exceeded
Test alert customization per worksite requirements

Step 5: Incident Reporting Integration

Worker witnesses or experiences safety incident
Voice command: "Report safety incident"
System prompts for incident details:
- Type: near-miss, minor injury, hazard identified, equipment failure
- Location and time (auto-captured)
- Description (voice recorded)
- People involved
- Immediate actions taken
Verify incident report generated instantly
Test automatic escalation to supervisor/safety officer
Verify QHSE compliance (Quality, Health, Safety, Environment)

Step 6: Confined Space Entry Protocols

Configure confined space work procedure:
- Gas testing required before entry
- Permit must be obtained
- Continuous monitoring required
- Safety watch must be present
Worker approaches confined space
System detects location (geofence or manual indication)
Verify system enforces protocol:
- "Confined space detected. Entry permit obtained?"
- "Atmospheric testing complete?"
- "Safety watch present?"
Verify entry blocked until all confirmations obtained
Monitor continuous gas levels during work (if sensor available)

Step 7: Hot Work Permit & Fire Safety

Configure hot work procedures (welding, grinding, cutting):
- Fire watch required
- Fire extinguisher within 3 meters
- Flammable materials cleared
- Ventilation adequate
Worker starts hot work
Verify system prompts compliance checks
Test fire risk assessment:
- "Clear 3-meter radius of flammables?"
- "Fire extinguisher location confirmed?"
Verify hot work logged with timestamp and location

Step 8: Lone Worker Safety

Configure lone worker mode:
- Periodic check-ins required (every 30 minutes)
- Automatic alerts if no response
- GPS location tracking active
Worker works alone for 2 hours
Verify system prompts: "Lone worker check-in - are you OK?"
Test manual check-in response
Simulate no response → verify automatic alert to supervisor:
- SMS/call with GPS location
- Escalating urgency if still no response
Verify worker can easily trigger duress alert: "Emergency" or panic button

Step 9: Customizable Safety Rules by Trade

Test pre-configured rule sets for different trades:
- Electricians: Lockout/tagout reminders, voltage testing
- Plumbers: Confined space protocols, water supply shutdown
- Builders: Fall protection checks, scaffold inspection
- Mechanics: Machine guarding, eye protection
Verify rule sets appropriate for trade
Test easy switching between rule sets for multi-trade workers

Step 10: Compliance Reporting & Audit Trail

Generate OH&S compliance report for day/week/month
Verify report includes:
- PPE compliance rate
- Checklist completion rate
- Safety reminders acknowledged
- Incidents reported
- Non-compliance events
Test export to safety management software
Verify audit trail tamper-proof

Pass Criteria

✅ PPE reminders triggered 100% when required
✅ Pre-start checklist enforced (cannot bypass)
✅ Time-based reminders accurate (±30 seconds)
✅ Hazard-specific alerts triggered appropriately
✅ Incident reporting ≤2 minutes to complete
✅ Confined space protocols enforced
✅ Hot work permit compliance tracked
✅ Lone worker check-ins functional and reliable
✅ Trade-specific rule sets appropriate
✅ Compliance reports complete and accurate
✅ Worker satisfaction ≥4.0/5.0 (helpful, not annoying)
✅ Safety officers find feature valuable ≥4.5/5.0

Failure Modes to Monitor

Safety reminders too frequent (annoyance)
Reminders too infrequent (missed hazards)
False hazard alerts cause alert fatigue
Checklist bypass possible
Incident reporting too cumbersome
Lone worker alerts fail to escalate
Compliance reports incomplete or inaccurate

TC-VAR-TX-003: AR Measurement Tools

Priority: P2 - High
Test Type: Software - Augmented Reality
Estimated Duration: 3 hours
Prerequisites: GF-TX with AR measurement module and calibrated cameras/sensors

Objective

Validate that AR measurement tools provide accurate distance, area, and angle measurements using the device's cameras and depth sensors, enabling hands-free measurement tasks.

Test Equipment

GF-TX device with calibrated ToF and cameras
Reference measuring tools: tape measure, laser measure, protractor
Test objects with known dimensions
Test environment with various measurement scenarios
2-3 trade workers for usability testing

Test Procedure

Step 1: Linear Distance Measurement

Place two markers at known distance apart:
- 0.5m, 1.0m, 2.0m, 3.0m, 5.0m
User activates AR measurement: "Measure distance"
User looks at first point and says "Start point"
User looks at second point and says "End point"
System displays distance on HUD
Compare to reference measurement
Repeat 10 times per distance

Target Accuracy:

0.5-2.0m: ±2% or ±2cm (whichever greater)
2.0-5.0m: ±3% or ±5cm (whichever greater)
5.0m: ±5% or ±10cm (whichever greater)

Step 2: Vertical Height Measurement

Measure ceiling height, wall height, door frame
Verify measurement from floor level:
- Look down at floor: "Floor"
- Look up at ceiling: "Ceiling"
- System calculates height
Compare to tape measure or laser measure
Test in various room sizes: 2.4m, 3.0m, 4.5m ceilings

Target Accuracy:

Ceiling height: ±5cm

Step 3: Area Measurement

Define rectangular area by corners:
- Look at corner 1: "Corner"
- Look at corner 2: "Corner"
- Look at corner 3: "Corner"
- Look at corner 4: "Corner"
System calculates area (m² or ft²)
Test with known areas:
- 1m × 1m = 1 m²
- 2m × 3m = 6 m²
- 4m × 5m = 20 m²
Compare to reference calculation

Target Accuracy:

Area: ±5% of actual area

Step 4: Angle Measurement

Measure angles using AR protractor:
- Corner angles (90°, 45°, 30°)
- Pipe bends
- Roof pitch
User defines three points:
- Vertex point
- Arm 1 end point
- Arm 2 end point
System displays angle in degrees
Compare to physical protractor

Target Accuracy:

Angles: ±3° for angles 30-150°

Step 5: Perimeter & Multi-Point Measurements

Trace perimeter of irregular shape:
- User marks 4+ points around shape
- System connects points and calculates perimeter
Test with irregular room shape
Verify perimeter calculation accurate

Step 6: Real-World Trade Scenarios

Electrician: Measure conduit run distance
- Measure from panel to outlet location
- Verify measurement within ±5%
Plumber: Measure pipe run and fittings
- Horizontal and vertical pipe segments
- Total run length accurate ±5%
Builder: Measure room dimensions for flooring
- Length × width for material estimate
- Area accurate ±5%
Painter: Measure wall area for paint calculation
- Total wall area (excluding doors/windows)
- Accurate ±10% (acceptable for estimates)

Step 7: Measurement Save & Annotation

After measurement, user can save:
- Voice label: "Living room width"
- Measurement stored with timestamp
- Location tagged (if GPS available)
Verify measurements accessible in history
Test export to notes or photos:
- AR measurement overlaid on photo
- Dimensions annotated clearly

Step 8: Multi-User Calibration

Test measurement accuracy with different users
Verify no significant inter-user variability (±2%)
Test that brief calibration improves accuracy:
- User holds device at known distance from wall (1m)
- System calibrates depth sensors
- Accuracy improves ≥20%

Step 9: Lighting Condition Robustness

Test measurements in varied lighting:
- Bright daylight
- Indoor fluorescent
- Dim lighting (evening)
- Mixed light sources
Verify accuracy degradation acceptable:
- Bright/indoor: full accuracy
- Dim: ±10% degradation acceptable
- System warns if confidence low

Step 10: Usability & Workflow Integration

Trade workers complete 5 measurement tasks using AR tools
Measure task completion time vs traditional tape measure
Target: AR measurement ≤150% time of tape measure
Collect feedback on:
- Ease of use
- Accuracy perception
- Hands-free benefit
- Would replace traditional tools for quick measurements?

Pass Criteria

✅ Linear distance accuracy: ±2-5% (depending on range)
✅ Vertical height accuracy: ±5cm
✅ Area measurement accuracy: ±5%
✅ Angle measurement accuracy: ±3°
✅ Perimeter calculation functional and accurate
✅ Trade scenario measurements useful (≥80% of workers agree)
✅ Measurement save and annotation functional
✅ Calibration improves accuracy ≥20%
✅ Lighting robustness acceptable (≤10% degradation in dim light)
✅ Task completion time ≤150% of tape measure
✅ User satisfaction ≥4.0/5.0
✅ ≥60% would use for quick measurements

Failure Modes to Monitor

Measurements wildly inaccurate ( > 10% error)
System fails in dim lighting
Angle measurements unreliable
Area calculations incorrect
Too slow to be practical vs tape measure
Calibration doesn't improve accuracy
Users find interface confusing

TC-VAR-TX-004: Job Documentation & Photo Capture

Priority: P2 - High
Test Type: Software - Field Documentation
Estimated Duration: 2 hours
Prerequisites: GF-TX with job documentation module and dual cameras

Objective

Validate that job documentation features enable trade workers to efficiently capture photos, annotate images, generate reports, and organize documentation for job records and client deliverables.

Test Equipment

GF-TX device
Simulated job sites (electrical, plumbing, construction)
Test scenarios requiring documentation
2 trade workers for usability testing

Test Procedure

Step 1: Hands-Free Photo Capture

Worker performing task needs photo documentation
Voice command: "Take photo" or "Capture"
Verify photo captured immediately:
- Shutter sound/haptic feedback
- HUD confirmation: "Photo saved"
Test burst mode: "Take 3 photos"
Verify photo quality:
- Resolution ≥12MP
- Proper exposure (not over/underexposed)
- Sharp focus
- Correct orientation

Step 2: Before/After Documentation

Configure before/after workflow:
- Voice: "Before photo"
- Complete work
- Voice: "After photo"
System auto-pairs images:
- Before/after side-by-side display
- Timestamp both images
Test multiple before/after sets per job:
- Organized by task or location

Step 3: AR Annotations on Photos

After capturing photo, add AR annotations:
- Voice: "Add note: Replaced circuit breaker"
- Arrow pointing to replaced component
- Text overlay with details
Test annotation types:
- Text labels
- Arrows and circles
- Distance measurements overlaid
- Issue flagging: "Problem: water leak detected"
Verify annotations saved with photo

Step 4: Job Organization & Folders

Configure job-based organization:
- Job name: "Smith Residence - Kitchen Reno"
- Client info: name, address, job number
All photos auto-filed to active job
Test folder structure:
- Job → Date → Task/Location
Verify easy navigation through folders
Test job switching: "Switch to job: Office HVAC"

Step 5: Automatic Time & Location Stamps

Verify every photo includes metadata:
- Timestamp (date + time)
- GPS location (if available)
- Job name
- User/worker name
Test metadata displayed in photo gallery
Verify metadata embedded in photo file (EXIF)
Test sorting photos by time or location

Step 6: Voice Narration & Notes

Capture photo and add voice note:
- "Take photo"
- "Add note: Customer requested additional outlet here"
Verify voice note transcribed to text (Whisper STT)
Test audio note playback if transcription unclear
Verify notes searchable

Step 7: Issue Flagging & Follow-Up

Worker discovers problem requiring attention:
- "Flag issue: Electrical panel corrosion"
System marks photo with "Issue" tag
Photo added to follow-up list
Test issue resolution workflow:
- View flagged issues
- Mark as resolved after fix
- Before/after documentation automatic

Step 8: Photo Report Generation

End of day/job, generate photo report:
- Voice: "Create job report"
System compiles:
- All photos from job
- Annotations and notes
- Before/after pairs
- Issue summaries
- Worker name and timestamps
Verify report formatted professionally:
- Title page with job info
- Photos organized by task or chronology
- Clean layout suitable for client
Test export formats:
- PDF (preferred)
- Slideshow
- ZIP archive of images

Step 9: Client Deliverable Quality

Review generated report for client presentation
Verify report includes:
- Company branding (if configured)
- Job details
- High-quality images
- Clear annotations
- Professional appearance
Test email delivery:
- Send report to client email
- Verify email sent successfully
- Test attachment size reasonable ( < 20 MB)

Step 10: Integration with Job Management Software

Test integration with common platforms:
- ServiceM8
- Fergus
- Tradify
- Simpro
Verify photos sync automatically:
- Upload to job record
- Client can view via platform
Test offline queueing:
- Photos captured offline
- Auto-upload when network available

Step 11: Storage & Backup

Verify local storage capacity:
- Estimate 500-1000 photos per week
- Storage sufficient for ≥4 weeks
Test cloud backup:
- Auto-backup to encrypted cloud (if enabled)
- Backup occurs on Wi-Fi only (save cellular data)
Verify photos not lost if device fails

Pass Criteria

✅ Photo capture ≤1 second from command
✅ Photo quality suitable for documentation (≥12MP, sharp, proper exposure)
✅ Before/after pairing functional and automatic
✅ AR annotations clear and easy to add
✅ Job organization logical and easy to navigate
✅ Time & GPS metadata accurate
✅ Voice notes transcribed ≥95% accurately
✅ Issue flagging and follow-up workflow functional
✅ Photo report generation ≤2 minutes
✅ Report quality professional and client-ready
✅ Email delivery functional
✅ Job management software integration works
✅ Storage capacity sufficient (≥1000 photos)
✅ Backup functional and reliable
✅ User satisfaction ≥4.5/5.0
✅ ≥80% would use for job documentation

Failure Modes to Monitor

Photo capture delayed or fails
Photo quality poor (blurry, dark, overexposed)
Annotations don't save with photos
Job organization confusing
Voice notes transcription inaccurate
Report generation fails or formatting poor
Sync to job management software fails
Storage fills up too quickly
Backup unreliable or photos lost

TC-VAR-TX-005: Remote Expert Assistance

Priority: P3 - Medium
Test Type: Software - Collaboration
Estimated Duration: 2 hours
Prerequisites: GF-TX with remote assistance module and network connectivity

Objective

Validate that remote expert assistance enables off-site supervisors or specialists to view the worker's camera feed, provide AR guidance overlays, and collaborate in real-time to solve technical problems or guide complex tasks.

Test Equipment

GF-TX device (worker)
Supervisor smartphone/tablet/PC with companion app
Test network: Wi-Fi and cellular (4G/LTE minimum)
Technical task requiring expert guidance
2 participants: worker and remote expert

Test Procedure

Step 1: Session Initiation & Connection

Worker needs assistance: Voice command "Request expert help"
System initiates call to designated expert/supervisor
Measure call establishment time (target: ≤10 seconds)
Verify notification sent to expert:
- Worker name
- Job location (GPS)
- Brief description (if provided)
Expert accepts call on companion app

Step 2: Live Camera Feed Streaming

Expert views worker's camera feed in real-time
Verify video quality:
- Resolution ≥720p preferred, ≥480p minimum
- Frame rate ≥15 fps preferred, ≥10 fps minimum
- Latency ≤2 seconds (acceptable for guidance)
Test with network conditions:
- Strong Wi-Fi: HD quality
- 4G cellular: SD quality acceptable
- 3G: degraded but functional
Verify video doesn't freeze or stutter excessively

Step 3: Two-Way Audio Communication

Worker and expert converse via audio
Verify audio quality:
- Clear both directions
- Low latency (≤500ms)
- Background noise suppression effective
Test bone conduction audio clarity for expert listening
Verify expert can hear worker clearly despite noisy job site

Step 4: AR Overlay & Annotation

Expert draws AR annotations on worker's HUD:
- Arrow: "Point to this component"
- Circle: "Check this area"
- Text label: "Tighten this bolt"
- Line/path: "Trace this wire"
Worker sees annotations overlaid on real-world view
Verify annotations:
- Appear in real-time (≤1 second delay)
- Accurately positioned (AR tracking stable)
- Clearly visible
Test annotation persistence:
- Annotations stay in place as worker moves head
- Expert can update or clear annotations

Step 5: Freeze Frame & Photo Sharing

Worker captures current view: "Freeze frame"
Expert sees frozen image and can annotate
Detailed markup without time pressure
Worker resumes live feed when ready
Test photo sharing:
- Worker can send specific photos to expert for review
- Expert can request worker take photo of something

Step 6: Expert-Guided Task Completion

Simulate complex task requiring step-by-step guidance:
- Electrician: Troubleshoot circuit using multimeter
- Plumber: Diagnose drain blockage location
- HVAC: Identify faulty compressor component
Expert provides verbal instructions and AR cues
Worker follows guidance
Measure task completion time vs worker alone
Verify task completed correctly (no errors)

Step 7: Screen Sharing (Optional Feature)

Expert shares diagram, schematic, or manual from their screen
Worker sees document overlaid or picture-in-picture on HUD
Expert can annotate shared document while worker views
Test useful for:
- Wiring diagrams
- Assembly instructions
- Reference photos

Step 8: Session Recording & Documentation

Verify session can be recorded (with consent):
- Video of worker's view
- Audio conversation
- AR annotations
Recording stored for:
- Training purposes
- Quality assurance
- Dispute resolution
Test recording playback:
- Clear video and audio
- Annotations visible
Verify privacy controls:
- Worker must consent to recording
- Indicator shows recording active

Step 9: Multi-Expert Consultation

Test scenario requiring multiple experts:
- Electrical + structural + fire safety
Verify up to 3 experts can join session
All experts see same camera feed
Annotations color-coded by expert
Audio conference functional for all participants

Step 10: Network Resilience & Reconnection

Simulate network interruption:
- Worker moves to area with poor signal
- Cellular handoff between towers
Verify graceful degradation:
- Video quality reduces automatically
- Audio maintains priority
- Session doesn't drop completely
Test automatic reconnection when signal improves

Step 11: Cost & Data Usage Monitoring

Measure data usage per session:
- Target: ≤100 MB per 15-minute session at SD quality
Verify data usage displayed to worker
Test Wi-Fi preference:
- System uses Wi-Fi when available
- Switches to cellular only if necessary
Alert if cellular data usage high

Step 12: Usability & Business Value Assessment

Worker and expert complete 3 assistance sessions
Collect feedback:
- Was remote assistance effective?
- Did it save time vs sending expert on-site?
- Were AR annotations helpful?
- Video/audio quality sufficient?
Calculate time savings:
- Expert travel time saved
- Problem resolved faster
Estimate cost savings:
- Typical call-out: 1-2 hours + travel
- Remote session: 15-30 minutes
- Cost reduction ≥50%

Pass Criteria

✅ Call establishment time ≤10 seconds
✅ Video quality ≥480p, ≥10 fps minimum
✅ Latency ≤2 seconds for video, ≤500ms for audio
✅ Two-way audio clear and low-latency
✅ AR annotations appear in real-time (≤1 sec delay)
✅ AR tracking stable (annotations stay positioned)
✅ Freeze frame and photo sharing functional
✅ Expert-guided tasks completed correctly
✅ Multi-expert consultation functional (≤3 experts)
✅ Network resilience acceptable (graceful degradation)
✅ Data usage reasonable (≤100 MB per 15 min SD)
✅ Session recording functional (if enabled)
✅ User satisfaction ≥4.5/5.0 (both worker and expert)
✅ Time savings ≥50% vs on-site visit
✅ ≥70% would use regularly for complex tasks

Failure Modes to Monitor

Call fails to establish or drops frequently
Video quality too poor to see details
Latency too high for real-time guidance
Audio communication unclear or choppy
AR annotations misaligned or invisible
Network interruption causes session loss
Data usage excessive
Workers find interface confusing
Experts can't provide effective guidance remotely

5. GF-DI: SilentLink (Deaf/Hard-of-Hearing) Variant

Target User: Deaf and hard-of-hearing individuals

TC-VAR-DI-001: Live Captioning Accuracy

Priority: P1 - Critical
Test Type: Software - Accessibility
Estimated Duration: 4 hours
Prerequisites: GF-DI with live captioning (Whisper STT) enabled

Objective

Validate that live captioning provides accurate, real-time transcription of speech with low latency, enabling Deaf and hard-of-hearing users to follow conversations, lectures, and environmental audio.

Test Equipment

GF-DI device
Test speakers (5 people with varying accents/speech patterns)
Reference transcriptions (ground truth)
Various acoustic environments (quiet, noisy, reverberant)
LibriSpeech or similar test audio corpus
3 Deaf/HoH test users for usability evaluation

Test Procedure

Step 1: Clean Audio Captioning (Quiet Environment)

Speaker reads prepared script at normal speaking rate
GF-DI displays live captions on HUD
Measure Word Error Rate (WER):
- Compare live captions to reference transcript
- Count substitutions, deletions, insertions
- Calculate WER = (S + D + I) / N × 100%
- Target: WER ≤5% for standard American accent
Measure latency:
- Time from speech to caption display
- Target: ≤500ms for real-time feel

Test Scenarios:

Monologue (one speaker)
Dialog (two speakers alternating)
Group conversation (3-4 speakers)

Step 2: Accent & Dialect Robustness

Test speakers with different accents:
- Standard American English
- British English (UK)
- Australian English
- Indian English
- Spanish-accented English
- African American Vernacular English (AAVE)
Measure WER per accent
Target accuracy:
- Standard accents: WER ≤5%
- Non-standard accents: WER ≤8%
- Heavy accents: WER ≤12% (acceptable degradation)

Step 3: Noisy Environment Captioning

Introduce background noise at varying levels:
- Low noise (SNR +20 dB): quiet office
- Moderate noise (SNR +10 dB): restaurant, café
- High noise (SNR +5 dB): busy street, crowd
Measure WER degradation vs clean audio
Target accuracy:
- SNR +20 dB: WER ≤6%
- SNR +10 dB: WER ≤15%
- SNR +5 dB: WER ≤30% (still partially usable)
Verify beamforming microphone array improves accuracy ≥20%

Step 4: Speaker Diarization (Who's Speaking?)

Group conversation with 3 speakers
Verify system identifies different speakers:
- "Speaker 1: How are you doing today?"
- "Speaker 2: I'm doing well, thanks!"
- "Speaker 3: Great to hear!"
Test speaker labels:
- Generic labels (Speaker 1, 2, 3) if unknown
- Named labels if speakers identified (future feature)
Accuracy: ≥90% correct speaker attribution

Step 5: Real-World Conversation Scenarios

One-on-one conversation:
- Two people discussing various topics
- Measure WER ≤5%
- Latency ≤500ms
Group meeting:
- 4-5 people in roundtable discussion
- Rapid speaker changes
- Overlapping speech
- Measure WER ≤10%
Lecture/presentation:
- Single speaker presenting for 15 minutes
- Verify captions don't overwhelm HUD
- Scrolling smooth and readable
Phone call:
- Captions for incoming phone audio
- Test via Bluetooth phone connection
- WER ≤8%

Step 6: Caption Display & Readability

Verify caption formatting:
- Text size comfortable (user adjustable)
- High contrast (white text on semi-transparent black)
- Positioned at bottom of HUD (doesn't obscure center view)
- Word wrap appropriate (no mid-word breaks)
Test caption scrolling:
- Smooth scrolling (not jerky)
- Speed adjustable (user preference)
- Previous text visible (scrollback buffer)
Verify punctuation and capitalization:
- Proper sentence boundaries
- Capital letters for names, start of sentences
- Punctuation aids comprehension

Step 7: Multi-Language Captioning

Test captioning in multiple languages:
- English (primary)
- Spanish
- Mandarin Chinese
- French
- German
Measure WER per language:
- Target: ≤8% for major languages
Test automatic language detection:
- Switches language when speaker changes to different language
- Accuracy: ≥90% language detection

Step 8: Captioning Customization

Test user-adjustable settings:
- Text size: Small, Medium, Large, Extra Large
- Background transparency: 0-100%
- Caption position: Bottom, Middle, Top
- Caption color: White, Yellow, Cyan
- Scroll speed: Slow, Medium, Fast
Verify settings save and persist across sessions

Step 9: Long-Duration Captioning Stability

Caption continuous speech for 1 hour
Verify no degradation in accuracy over time
Check for memory leaks or performance issues
Verify battery usage acceptable (≥4 hours continuous captioning)

Step 10: User Evaluation (Deaf/HoH)

Three Deaf or hard-of-hearing users test captioning in real-world scenarios:
- Conversation with hearing friend/family
- Watching TV or video
- Attending meeting or class
Collect feedback:
- Caption accuracy perception (1-5 scale)
- Latency acceptable? (yes/no)
- Caption display readable? (yes/no)
- Would use regularly? (yes/no)
Identify usability issues and improvement areas

Pass Criteria

✅ WER ≤5% in quiet environment (standard accent)
✅ WER ≤8% for UK/AU/Indian English accents
✅ WER ≤15% at SNR +10 dB (moderate noise)
✅ Latency ≤500ms (real-time feel)
✅ Speaker diarization ≥90% accurate
✅ Caption display readable and comfortable
✅ Multi-language support functional (≥5 languages)
✅ Settings customizable and persistent
✅ No degradation after 1-hour continuous use
✅ Battery life ≥4 hours continuous captioning
✅ Deaf/HoH users rate accuracy ≥4.0/5.0
✅ Deaf/HoH users rate usability ≥4.2/5.0
✅ ≥80% would use regularly

Failure Modes to Monitor

WER too high (captions incomprehensible)
Latency too high (conversation hard to follow)
Speaker changes not detected
Caption display unreadable or obstructs view
Multi-language detection incorrect
System crashes during long captioning sessions
Battery drains too quickly

TC-VAR-DI-002: Sound-Class Alerts & Directional Audio

Priority: P1 - Critical
Test Type: Software - Environmental Awareness
Estimated Duration: 3 hours
Prerequisites: GF-DI with sound classification AI enabled

Objective

Validate that sound classification accurately detects important environmental sounds (doorbell, alarms, sirens, etc.) and provides visual/haptic alerts with directional information to enhance situational awareness for Deaf users.

Test Equipment

GF-DI device
Sound library: doorbell, phone ring, alarm clock, fire alarm, baby crying, dog barking, car horn, siren, knock on door
Speakers to play sounds from different directions
Anechoic or controlled acoustic environment
360° positioning system for directional testing

Test Procedure

Step 1: Sound Classification Accuracy

Play each sound type 10 times at varying volumes
Verify correct classification for each sound:
- Doorbell → "Doorbell detected"
- Phone ringing → "Phone ringing"
- Alarm clock → "Alarm detected"
- Fire alarm → "Fire alarm! Evacuate"
- Baby crying → "Baby crying"
- Dog barking → "Dog barking"
- Car horn → "Car horn"
- Siren → "Emergency siren nearby"
- Knock on door → "Knock on door"
Measure classification accuracy per sound type

Target Accuracy:

Critical safety sounds (fire alarm, siren): ≥98%
Important sounds (doorbell, phone, baby): ≥95%
General sounds (dog, knock): ≥90%

Step 2: Alert Display & Notification

When sound detected, verify alert displayed on HUD:
- Visual icon (doorbell symbol, phone symbol, etc.)
- Text label: "Doorbell detected"
- Timestamp
- Directional indicator (arrow pointing to sound source)
Test haptic feedback:
- Distinct vibration patterns per sound type
- Fire alarm: Rapid pulsing
- Doorbell: Two short pulses
- Phone: Continuous vibration
Verify alert urgency levels:
- Critical (fire alarm): Red, flashing, strong haptic
- High (baby crying, siren): Yellow, persistent, medium haptic
- Medium (doorbell, phone): Green, brief, light haptic

Step 3: Directional Sound Localization

Play sound from different directions:
- Front (0°)
- Left (90°)
- Right (270°)
- Behind (180°)
- Diagonal positions (45°, 135°, 225°, 315°)
Verify directional arrow on HUD points toward sound source
Measure directional accuracy:
- Target: ±30° for front/back, ±45° for sides
Test with multiple microphones (dual-mic beamforming)

Step 4: Distance/Proximity Estimation

Play sound at varying distances:
- Near: 1-2 meters
- Medium: 3-5 meters
- Far: 6-10 meters
Verify system indicates proximity:
- "Doorbell nearby"
- "Dog barking - medium distance"
- "Siren far away"
Test relative loudness indication (not required to be precise)

Step 5: Multi-Sound Environment

Play multiple sounds simultaneously:
- Doorbell + dog barking
- Phone ringing + baby crying
- Background TV + doorbell
Verify system detects and alerts for both sounds
Test priority system:
- Critical sounds (fire alarm) override others
- Multiple alerts displayed simultaneously (up to 3)
Ensure display not cluttered or confusing

Step 6: Continuous Monitoring & Power Efficiency

Enable sound detection for 8-hour period (simulated day)
Verify system continuously monitors audio
Test alerts triggered consistently throughout day
Measure battery impact:
- Target: ≤15% additional battery drain vs baseline
Verify no missed alerts during long monitoring

Step 7: False Positive Minimization

Play non-target sounds that may trigger false alarms:
- Similar tones (microwave beep vs alarm)
- Voice speech (not classified as door knock)
- Music or TV sounds
Measure false positive rate per sound class
Target: False positive rate ≤5% overall

Step 8: User-Customizable Alerts

Test customization options:
- Enable/disable specific sound classes
- Adjust sensitivity per sound type
- Custom haptic patterns
- Alert priority levels
User scenario: Disable dog barking alerts (annoying), keep baby crying alerts
Verify customization respected

Step 9: Alert History & Review

View alert history (past 24 hours)
Verify log includes:
- Sound type
- Timestamp
- Direction
- User response (acknowledged/dismissed)
Test filtering by sound type or time
Useful for identifying patterns (e.g., doorbell rang while you were out)

Step 10: Real-World User Evaluation

Deaf users wear GF-DI for 1-2 days in home environment
Test detects real-world sounds:
- Actual doorbell
- Actual phone
- Kitchen timer/microwave
- Smoke detector test
Collect feedback:
- Useful sounds detected? (yes/no per sound type)
- False alarms? (describe)
- Directional arrows helpful? (yes/no)
- Would rely on this for safety? (yes/no)

Pass Criteria

✅ Sound classification accuracy: Fire alarm/siren ≥98%, Important sounds ≥95%, General sounds ≥90%
✅ Alerts displayed within 1 second of sound onset
✅ Haptic feedback distinct per sound type
✅ Directional accuracy ±30-45°
✅ Multi-sound detection functional (≤3 simultaneous)
✅ Priority system prevents alert overload
✅ Battery drain ≤15% vs baseline
✅ False positive rate ≤5%
✅ Customization options functional and respected
✅ Alert history complete and accurate
✅ Deaf users find feature useful ≥4.5/5.0
✅ ≥90% would rely on this for home safety

Failure Modes to Monitor

Critical sounds (fire alarm) missed
Excessive false positives cause alert fatigue
Directional arrows inaccurate or misleading
Alerts delayed ( > 2 seconds)
Battery drains too quickly
Multiple sounds cause system overload
Customization doesn't work

TC-VAR-DI-003: Sign Language Recognition (Future Feature)

Priority: P3 - Medium
Test Type: Software - AI Vision
Estimated Duration: 4 hours
Prerequisites: GF-DI with camera-based sign language recognition (if available)

Objective

If sign language recognition is implemented, validate that the system can accurately recognize common sign language gestures and translate them to text/speech, enabling communication between Deaf and hearing individuals.

Note: This is a highly advanced feature and may not be in MVP. If not available, skip this test case.

Test Equipment

GF-DI device with front-facing camera
Deaf signer (ASL or local sign language)
Reference sign language database (ASL fingerspelling, common signs)
Hearing conversation partner

Test Procedure

Step 1: Fingerspelling Recognition

Signer performs ASL fingerspelling alphabet (A-Z)
System recognizes each letter and displays on HUD
Measure accuracy per letter
Target: ≥95% accuracy for clear fingerspelling at moderate speed

Step 2: Common Sign Recognition

Test recognition of 50-100 common signs:
- Greetings: Hello, goodbye, nice to meet you
- Questions: What, where, when, who, why, how
- Common words: Yes, no, please, thank you, sorry
- Numbers: 1-10
Measure accuracy per sign
Target: ≥90% accuracy for common vocabulary

Step 3: Sentence Construction

Signer performs multi-sign sentences:
- "Where is the bathroom?"
- "I need help."
- "What time is it?"
System translates to English text displayed on HUD
Verify sentence structure reasonably accurate

Step 4: Real-Time Translation Speed

Measure latency from sign completion to text display
Target: ≤1 second per sign
Verify smooth real-time conversation possible

Step 5: Bi-Directional Communication

Hearing person speaks → live captions for Deaf person
Deaf person signs → translated text/speech for hearing person
Simulate real conversation workflow

Step 6: User Evaluation

Deaf signers test sign recognition
Collect feedback on:
- Accuracy perception
- Usefulness for communication
- Preferred over manual typing?

Pass Criteria (if feature implemented)

✅ Fingerspelling accuracy ≥95%
✅ Common sign accuracy ≥90%
✅ Sentence translation reasonably accurate
✅ Latency ≤1 second per sign
✅ Bi-directional communication functional
✅ User satisfaction ≥4.0/5.0

Failure Modes to Monitor

Sign recognition inaccurate
Latency too high for conversation
Limited vocabulary coverage
Doesn't work with different signing styles

TC-VAR-DI-004: Vibration Haptic Customization

Priority: P2 - High
Test Type: Hardware/Software - Accessibility
Estimated Duration: 2 hours
Prerequisites: GF-DI with haptic motors enabled

Objective

Validate that haptic vibration feedback is customizable, provides distinct patterns for different alert types, and enables Deaf users to perceive important notifications without visual attention.

Test Equipment

GF-DI device with haptic motors
3 Deaf/HoH test users
Various alert scenarios (doorbell, phone, alarm, message)

Test Procedure

Step 1: Haptic Pattern Distinctiveness

Configure distinct vibration patterns for alert types:
- Doorbell: Two short pulses
- Phone call: Continuous vibration (escalating intensity)
- Alarm: Rapid pulsing (0.2 sec on, 0.2 sec off)
- Message: Single short pulse
- Fire alarm: Very rapid pulsing (urgent)
User receives each alert type randomly (10 times each)
User identifies alert type by haptic pattern only (eyes closed)
Measure identification accuracy per alert type
Target: ≥90% correct identification

Step 2: Haptic Intensity Customization

Test adjustable intensity levels: Low, Medium, High
User sets preferred intensity for each alert type
Verify settings respected
Test intensity range:
- Low: Noticeable but subtle
- Medium: Clear and comfortable
- High: Strong but not painful

Step 3: Haptic Location (if multiple motors)

If multiple haptic motors (left/right temples):
- Test directional haptic for sound localization
- Left sound → left temple vibrates
- Right sound → right temple vibrates
Verify directional haptics enhance sound awareness

Step 4: Haptic Escalation

Test escalating intensity for ignored alerts:
- First alert: Medium intensity
- After 10 sec: Increase to high intensity
- After 30 sec: Maximum intensity (if critical)
User can acknowledge alert to stop escalation

Step 5: Haptic Patterns for Activities

Test haptic feedback for non-alert scenarios:
- Navigation turn-by-turn: Left/right pulses
- Confirmation feedback: Single short pulse
- Error/warning: Two short pulses
- Timer countdown: Pulsing increases frequency as time runs out
Verify patterns intuitive and not confusing

Step 6: Battery Impact of Haptics

Measure battery drain with frequent haptic alerts:
- 100 alerts per day (typical usage)
Verify battery impact acceptable:
- Target: ≤5% additional battery drain

Step 7: Long-Term Comfort

User wears GF-DI for full day with haptic alerts enabled
Collect feedback on:
- Haptic comfort (not annoying or painful)
- Fatigue or irritation
- Would use haptics regularly?

Step 8: User Customization Options

Test customizable settings:
- Pattern selection per alert type
- Intensity per alert type
- Enable/disable haptics globally
- Quiet hours (disable haptics during sleep)
Verify settings save and persist

Pass Criteria

✅ Haptic pattern identification ≥90% accurate
✅ Intensity levels noticeable and comfortable
✅ Directional haptics functional (if available)
✅ Escalation appropriate and not annoying
✅ Haptic feedback intuitive for activities
✅ Battery impact ≤5%
✅ Comfortable for full-day use
✅ Customization options functional
✅ User satisfaction ≥4.3/5.0
✅ ≥85% would use haptics regularly

Failure Modes to Monitor

Patterns not distinct enough (confusion)
Intensity too weak or too strong
Escalation annoying
Battery drain excessive
Haptics cause discomfort or headache
Customization doesn't work

TC-VAR-DI-005: Emergency Communication System

Priority: P1 - Critical
Test Type: Software - Safety
Estimated Duration: 2 hours
Prerequisites: GF-DI with emergency communication features enabled

Objective

Validate that emergency communication features enable Deaf users to contact emergency services, communicate their situation via text-to-speech, and receive visual emergency guidance.

Test Equipment

GF-DI device
Emergency services simulation (not real 911 calls)
Test scenarios requiring emergency assistance

Test Procedure

Step 1: Emergency SOS Activation

User triggers SOS: Triple-press button or voice "Emergency"
Verify SOS activated within 1 second
System initiates emergency call (test mode, not real 911)
Measure call establishment time: ≤10 seconds

Step 2: Text-to-Speech Communication

User types or selects pre-configured emergency messages:
- "I am deaf. I need help. Send text messages."
- "Medical emergency. Unconscious person."
- "Fire. Evacuate building."
System converts text to speech and plays to emergency operator
Verify speech clear and understandable
Test message delivery confirmed

Step 3: Live Transcription of Operator

Emergency operator responds with questions
System transcribes operator's speech to text
User reads captions on HUD in real-time
User types response → converted to speech for operator
Verify two-way communication functional

Step 4: Location Sharing

Verify GPS location automatically sent with SOS
Accuracy: ≤10m outdoor, ≤50m indoor
Test location updates if user moves
Verify location visible to emergency services

Step 5: Medical Information Sharing

Pre-configured medical info sent with SOS:
- Deaf (hearing impairment)
- Allergies
- Medications
- Emergency contacts
Verify info displayed to operator
Test that critical info prominently displayed

Step 6: Pre-Configured Quick Messages

User can select from quick messages during emergency:
- "I cannot hear you. Please send text."
- "Yes"
- "No"
- "Send ambulance"
- "Send police"
- "Send fire department"
Verify messages sent instantly ( < 1 second)
Test that messages clearly communicated

Step 7: Video Call Option (if available)

If supported, test video call to emergency services
User can show situation via camera
Operator can see user's environment
Useful for sign language communication
Verify video quality sufficient

Step 8: Emergency Guidance (Fire, Earthquake, etc.)

If fire alarm detected, system provides visual guidance:
- "Fire alarm detected. Evacuate building."
- Directions to nearest exit (if indoor mapping available)
- Flashing visual indicators
Test guidance clear and actionable

Step 9: False Alarm Cancellation

User accidentally triggers SOS
Verify 5-second cancellation window
User can cancel: "Cancel SOS" or button press
System sends cancellation to emergency services
Verify no false emergency response dispatched

Step 10: Emergency Contact Alerts

When SOS triggered, alerts sent to emergency contacts:
- SMS, push notification, call
Emergency contacts receive:
- User's location
- Nature of emergency (if specified)
- Status updates
Verify alerts delivered within 10 seconds

Pass Criteria

✅ SOS activates within 1 second
✅ Emergency call establishes ≤10 seconds
✅ Text-to-speech clear and understandable
✅ Live transcription functional (WER ≤10%)
✅ GPS location accurate and sent automatically
✅ Medical info shared correctly
✅ Quick messages send instantly
✅ Video call functional (if supported)
✅ Emergency guidance clear and actionable
✅ False alarm cancellation functional (5-sec window)
✅ Emergency contact alerts delivered ≤10 seconds
✅ User satisfaction ≥4.5/5.0
✅ ≥95% would rely on this for emergencies

Failure Modes to Monitor

SOS fails to activate
Call doesn't connect
Text-to-speech inaudible or garbled
Transcription inaccurate (operator's speech)
Location inaccurate or missing
Medical info not sent
False alarm sends emergency response
Emergency contacts not alerted

6. GF-VI: VisionAssist (Low Vision/Blind) Variant

Target User: Individuals with low vision, legal blindness, vision impairments

TC-VAR-VI-001: Scene Description Quality and Accuracy

Priority: P1 - Critical
Test Type: AI - Computer Vision
Estimated Duration: 4 hours
Prerequisites: GF-VI variant with scene description AI enabled

Objective

Validate that AI-generated scene descriptions provide accurate, useful, and contextually appropriate information for low-vision and blind users navigating their environment.

Test Equipment

GF-VI variant
Diverse test environments (indoor/outdoor)
Ground truth labels (human annotators)
Low-vision test participants (3-5 users)
Video recording equipment

Test Procedure

Step 1: Static Scene Description Accuracy

Position user in 20 different static scenes:
- Living room
- Kitchen
- Office
- Street corner
- Store entrance
- Park bench
- Bus stop
- Restaurant table
- Bedroom
- Bathroom
User requests scene description: "Describe what's around me"
Record AI-generated description
Compare with ground truth human description
Measure accuracy metrics:
- Object detection accuracy ≥85%
- Spatial relationship accuracy ≥80%
- Distance estimation accuracy ±20%
- Color identification accuracy ≥90%

Step 2: Dynamic Scene Description (Moving Environments)

User walks through dynamic environments:
- Busy sidewalk
- Shopping mall
- Office corridor
- Public transit station
System provides continuous scene updates
Verify system identifies:
- Moving people and vehicles
- Doorways and exits
- Stairs and obstacles
- Signage and wayfinding cues
Update latency ≤2 seconds for significant changes

Step 3: Contextual Appropriateness

Test that descriptions prioritize relevant information:
- In kitchen: appliances, counters, hazards
- On street: crosswalk, traffic, obstacles
- In store: aisles, products, checkout
Verify safety-critical information mentioned first
Descriptions should be concise (≤30 seconds)
No overwhelming detail or information overload

Step 4: Person and Face Detection Ethics

Test person detection:
- "Someone is 2 meters ahead"
- "Three people walking toward you"
Verify system does NOT identify faces or names (privacy)
System should describe:
- Approximate distance
- Direction of movement
- General position (ahead/left/right)
Never: race, gender assumption, identification

Step 5: Object Identification for Daily Tasks

User asks specific questions:
- "Where is my phone?"
- "What's on the table?"
- "Find the remote control"
- "Is there a chair nearby?"
Verify AI can identify and locate common objects
Directional guidance: "Your phone is on the table to your right"
Object identification accuracy ≥90%

Step 6: Lighting Condition Robustness

Test scene description in varied lighting:
- Bright daylight
- Indoor artificial light
- Dim evening light
- Near darkness (with IR assist if available)
System should indicate poor lighting: "Scene is very dark"
Description quality should degrade gracefully
Critical obstacles still detected

Step 7: User Satisfaction and Utility

After testing, survey users (3-5 blind/low-vision participants)
Questions:
- "Were descriptions accurate?" (1-5 scale)
- "Did descriptions help you understand your surroundings?" (1-5 scale)
- "Were descriptions provided quickly enough?" (1-5 scale)
- "Would you rely on this for daily navigation?" (Yes/No)
Conduct 30-minute real-world task:
- Navigate unfamiliar indoor space
- Find specific room or object
- Avoid obstacles
Observer records: successful completion, confidence, safety

Pass Criteria

✅ Object detection accuracy ≥85%
✅ Spatial relationship accuracy ≥80%
✅ Distance estimation ±20%
✅ Color identification ≥90%
✅ Dynamic scene updates ≤2 seconds
✅ Safety-critical information prioritized
✅ Description length ≤30 seconds
✅ No face identification or privacy violations
✅ Object identification accuracy ≥90%
✅ Graceful degradation in poor lighting
✅ User satisfaction ≥4.0/5.0
✅ ≥80% would rely on this for daily use

Failure Modes to Monitor

Inaccurate object identification
Missing critical obstacles
Overwhelming or verbose descriptions
Delayed scene updates
Privacy violations (face identification)
Poor performance in low light
Confusing spatial descriptions

Priority: P1 - Critical
Test Type: AI - OCR and Text-to-Speech
Estimated Duration: 3 hours
Prerequisites: GF-VI variant, OCR engine (Tesseract or similar)

Objective

Validate that the system can accurately read text from signs, labels, documents, and screens, providing blind and low-vision users with access to written information in their environment.

Test Equipment

GF-VI variant
Sample text materials:
- Street signs
- Product labels
- Restaurant menus
- Medicine bottles
- Bus schedules
- Documents (printed, handwritten)
- Digital screens
Ground truth transcriptions

Test Procedure

Step 1: Printed Text Recognition Accuracy

User points camera at 30 printed text samples:
- Street signs (10 samples)
- Product packaging (5 samples)
- Menus (5 samples)
- Medicine labels (5 samples)
- Documents (5 samples)
User commands: "Read this" or "What does this say?"
System performs OCR and reads text aloud via TTS
Measure:
- Character Error Rate (CER) ≤5%
- Word Error Rate (WER) ≤8%
- Reading time ≤3 seconds per line
Compare TTS output with ground truth

Step 2: Varied Font and Size Handling

Test text in varied fonts:
- Sans-serif (Arial, Helvetica)
- Serif (Times New Roman)
- Decorative/script fonts
- Handwriting (print and cursive)
Test varied text sizes:
- Large (≥24 pt)
- Medium (12-24 pt)
- Small (≤12 pt)
Verify system can read all with acceptable accuracy
System should warn: "Text is very small, move closer"

Step 3: Lighting and Angle Robustness

Test OCR under challenging conditions:
- Low light / shadows
- Glare / reflections
- Angled text (not perpendicular to camera)
- Curved surfaces (bottles, cans)
System should provide feedback:
- "Text detected but unclear, adjust angle"
- "Lighting too low, please use brighter area"
Measure accuracy degradation

Step 4: Real-Time Text Guidance

User scans environment for text
System provides real-time guidance:
- "Text detected ahead, move camera slightly left"
- "Text in focus, reading now"
Haptic pulse when text in optimal position
Verify guidance helps user capture text quickly (≤5 seconds to align)

Step 5: Document Reading Mode

User places multi-page document on flat surface
Activates document reading mode
System reads continuously line by line
User can pause/resume: "Pause" or "Continue"
User can navigate: "Next page" or "Previous line"
Verify:
- Smooth continuous reading
- No skipped lines
- Correct reading order (top-to-bottom, left-to-right)

Step 6: Medicine Bottle Label Safety

Test 10 medicine bottles with critical information:
- Drug name
- Dosage
- Warnings
- Expiration date
Verify system prioritizes safety information:
- Warnings read first
- Dosage clearly stated
- Expiration date emphasized
Accuracy must be 100% for critical information
Any uncertainty: system should say "I'm not certain, please verify with sighted assistance"

Step 7: Screen and Digital Text Reading

User points camera at digital screens:
- Computer monitor
- Phone screen
- ATM display
- Digital kiosk
System reads on-screen text
Verify:
- Can handle varied screen brightness
- No flicker interference
- Updates when screen content changes

Step 8: User Satisfaction and Real-World Utility

Survey blind/low-vision users (3-5 participants)
Real-world tasks:
- Read medicine bottle
- Read restaurant menu
- Read street sign for navigation
- Read bus schedule
Measure:
- Task success rate ≥90%
- User confidence rating ≥4.0/5.0
- Would use for daily tasks: ≥85% yes

Pass Criteria

✅ Printed text CER ≤5%, WER ≤8%
✅ Reading time ≤3 seconds per line
✅ Handles varied fonts and sizes adequately
✅ Provides helpful guidance for text alignment
✅ User can align text ≤5 seconds with guidance
✅ Document reading mode smooth and accurate
✅ Medicine label safety: 100% accuracy on critical info
✅ Digital screen text readable
✅ Real-world task success ≥90%
✅ User confidence ≥4.0/5.0
✅ ≥85% would use for daily tasks

Failure Modes to Monitor

High character/word error rates
Slow reading performance
Missing critical safety information
Inaccurate guidance (misaligned text)
Skipped lines in document mode
Can't handle digital screens
Low user confidence in accuracy

Priority: P1 - Critical
Test Type: Integration - Navigation + Haptic
Estimated Duration: 4 hours
Prerequisites: GF-VI variant, ToF/LiDAR sensors, haptic motors

Objective

Validate that the navigation system provides blind and low-vision users with safe, accurate, and intuitive guidance using audio cues, haptic feedback, and spatial instructions.

Test Equipment

GF-VI variant
Test navigation routes (indoor and outdoor)
Observer to monitor safety
Stopwatch
Obstacle course materials

Test Procedure

Step 1: Obstacle Detection and Haptic Alerts

User navigates obstacle course blindfolded:
- Standing obstacles at head/chest/shin height
- Floor obstacles (boxes, bags)
- Overhanging obstacles
- Stairs up and down
System provides haptic alerts:
- Obstacle ahead: steady pulse
- Obstacle left: left-side pulse
- Obstacle right: right-side pulse
- Stairs: rapid pulse pattern
Verify:
- Obstacle detection rate ≥98%
- Alert timing: ≥1.5 seconds before collision
- Haptic patterns distinguishable
- User successfully avoids obstacles

Step 2: Audio Navigation Instructions

User navigates unfamiliar route with verbal guidance
System provides turn-by-turn instructions:
- "In 5 meters, turn right"
- "Door ahead on your left"
- "Stairs descending in 3 meters"
Verify:
- Instructions clear and timely
- Distance estimates accurate ±20%
- User reaches destination successfully
- Minimal confusion or backtracking

Step 3: Indoor Navigation and Wayfinding

User navigates indoor space:
- Office building: find conference room
- Shopping mall: find specific store
- Hospital: find radiology department
System uses:
- Turn-by-turn voice guidance
- Landmark identification: "Elevator lobby ahead"
- Distance to destination
Measure:
- Task completion rate ≥85%
- Time to destination (compare vs sighted person)
- User reports feeling safe and confident

Step 4: Outdoor Navigation and Street Crossing

User navigates outdoor urban route:
- Walk 3 blocks
- Cross 2 intersections
- Navigate around pedestrians
System provides:
- Crosswalk detection
- Traffic signal status (if detectable)
- Pedestrian proximity warnings
- Guidance to stay on sidewalk
Verify:
- User stays on safe path
- Successfully crosses streets (with safety observer)
- Warns of approaching vehicles
- Never guides into unsafe situations

Step 5: Combined Haptic + Audio Multimodal Guidance

User navigates complex environment with both modalities:
- Haptic: immediate proximity obstacles
- Audio: navigation instructions and context
Verify modalities complement (not conflict):
- Haptic for urgent/immediate hazards
- Audio for contextual information and navigation
User can distinguish and respond to both simultaneously
No cognitive overload: user reports ≤3/5 difficulty

Step 6: Customization and User Preference

User can customize haptic intensity:
- Low / Medium / High
User can customize verbosity:
- Minimal (only essential info)
- Standard
- Detailed (more context)
Verify settings persist and work as expected

Step 7: Real-World Navigation Task

User performs authentic navigation task:
- Navigate from home to nearby store
- Enter store, find specific product, navigate to checkout
- Return home
Minimal intervention from observer (only for safety)
Measure:
- Task success
- Time taken
- Number of guidance errors
- User confidence rating

Pass Criteria

✅ Obstacle detection ≥98%
✅ Alert timing ≥1.5 seconds before collision
✅ User avoids obstacles successfully
✅ Audio instructions clear and timely
✅ Distance estimates ±20%
✅ Indoor navigation success ≥85%
✅ Outdoor navigation safe (no unsafe situations)
✅ Haptic + audio modalities complement each other
✅ Cognitive load ≤3/5
✅ Customization settings work correctly
✅ Real-world navigation task successful
✅ User confidence ≥4.0/5.0

Failure Modes to Monitor

Missed obstacle detection
Late or missing alerts
Confusing navigation instructions
Inaccurate distance estimates
Guidance into unsafe situations
Conflicting haptic and audio cues
Cognitive overload
Settings don't persist

TC-VAR-VI-004: Object Recognition and Product Identification

Priority: P2 - High
Test Type: AI - Computer Vision
Estimated Duration: 3 hours
Prerequisites: GF-VI variant, object detection AI

Objective

Validate that the system can identify common objects and products, enabling blind and low-vision users to independently locate and identify items in their environment.

Test Equipment

GF-VI variant
50+ common household and product items
Diverse object categories
Test participants (3-5 blind/low-vision users)

Test Procedure

Step 1: Common Object Identification Accuracy

Present 50 common objects:
- Kitchen: mug, plate, fork, bottle, can
- Living room: remote, book, pillow, lamp
- Bedroom: alarm clock, phone charger, headphones
- Bathroom: toothbrush, soap, towel, hairbrush
- Office: pen, stapler, scissors, tape
User asks: "What is this?" or "Identify object"
System identifies object within 2 seconds
Measure:
- Top-1 accuracy ≥85%
- Top-3 accuracy ≥95%
- False identification rate < 5%

Step 2: Product and Brand Recognition (Barcode/Package)

Present 30 product packages:
- Food items (cereal, soup, snacks)
- Beverages (juice, soda, water)
- Household products (detergent, soap)
- Medicine (over-the-counter)
User requests: "What product is this?"
System scans barcode/package and identifies:
- Product name
- Brand
- Size/quantity
- Key features (e.g., "Gluten-free")
Measure accuracy ≥90%

Step 3: Multi-Object Scenes

Present scenes with multiple objects:
- Kitchen counter: 5-8 items
- Desktop: 4-6 items
- Shelf: 6-10 items
User asks: "What's on the counter?"
System lists all detected objects
Verify:
- All objects identified (recall ≥85%)
- No false objects mentioned (precision ≥90%)
- Spatial layout described: "Fork is to the left of the plate"

Step 4: Object Search and Location Guidance

User asks: "Where is my phone?"
System scans environment and locates phone
Provides directional guidance:
- "Your phone is on the table, 2 meters ahead and slightly to your right"
User reaches for phone with guidance
Verify:
- Object located ≥85% of time
- Directional guidance accurate ±15°
- Distance guidance ±30 cm
- User retrieves object successfully ≥80%

Step 5: Currency and Money Identification

Present various currency denominations:
- Bills: $5, $10, $20, $50, $100 (or local currency)
- Coins: (if supported)
User asks: "How much is this?"
System identifies denomination correctly
Accuracy must be 100% (financial safety critical)
If uncertain: "I'm not sure, please verify with sighted assistance"

Step 6: Color Identification for Clothing

User holds up clothing item
Asks: "What color is this shirt?"
System identifies primary color(s):
- Solid colors (red, blue, black, white, etc.)
- Patterns (striped, checkered, etc.)
Measure color accuracy ≥90%
User feedback: helpful for matching outfits

Step 7: Real-World Utility Testing

User performs daily tasks:
- Find specific item in refrigerator
- Locate keys on cluttered table
- Identify correct medicine bottle from several
- Select matching socks from drawer
Measure:
- Task success rate ≥80%
- Time to complete vs baseline
- User satisfaction ≥4.0/5.0

Pass Criteria

✅ Object identification top-1 accuracy ≥85%
✅ Object identification top-3 accuracy ≥95%
✅ False identification rate < 5%
✅ Product recognition accuracy ≥90%
✅ Multi-object recall ≥85%, precision ≥90%
✅ Object location and retrieval success ≥80%
✅ Currency identification 100% accurate
✅ Color identification ≥90%
✅ Real-world task success ≥80%
✅ User satisfaction ≥4.0/5.0

Failure Modes to Monitor

Incorrect object identification
Missed objects in multi-object scenes
Inaccurate location guidance
Currency misidentification (critical)
Poor color identification
Low user confidence in results

TC-VAR-VI-005: Accessibility UI and Voice Control Usability

Priority: P1 - Critical
Test Type: Usability - UI/UX
Estimated Duration: 3 hours
Prerequisites: GF-VI variant, accessibility features enabled

Objective

Validate that the user interface is fully accessible to blind and low-vision users through voice control, audio feedback, and simplified interaction patterns.

Test Equipment

GF-VI variant
Test participants (5+ blind/low-vision users)
Task scenarios
Observer notes

Test Procedure

Step 1: Voice Command Recognition and Response

User performs 30 common voice commands:
- "Describe scene"
- "Read text"
- "Find my phone"
- "What time is it?"
- "Check battery"
- "Call [contact]"
- "Send message to [contact]"
- "Set timer for 10 minutes"
- "Navigate to [address]"
- "Increase volume"
Measure:
- Command recognition accuracy ≥95%
- Response latency ≤1 second
- Appropriate action taken for each command
Test with varied speech patterns and accents

Step 2: Audio Feedback and Confirmation

All system actions provide audio confirmation:
- "Taking photo"
- "Timer set for 10 minutes"
- "Message sent to Mom"
- "Low battery, 15% remaining"
Verify:
- Audio feedback provided for all actions
- Feedback concise (≤3 seconds)
- Feedback clear and understandable
User never left wondering if action completed

Step 3: Menu Navigation Without Visual Cues

User navigates system settings menu using only audio:
- "Open settings"
- System reads menu options one by one
- User says "Next" to hear next option
- User says "Select" to choose option
Verify:
- User can navigate entire menu structure
- Can reach any setting without visual aid
- Menu navigation intuitive (≤5 commands to reach target)

Step 4: Error Handling and Recovery

Simulate error conditions:
- Unrecognized command
- Action failed (e.g., no network for call)
- Invalid input
System provides clear error messages:
- "I didn't understand that, please try again"
- "Cannot make call, no network connection"
- "Invalid contact name"
Verify:
- Error messages clear and actionable
- User knows what went wrong
- User knows how to recover

Step 5: Gesture-Free Interaction

User completes all tasks using voice only (no touch/gestures)
Tasks:
- Make phone call
- Send text message
- Set alarm
- Check weather
- Request navigation
Verify 100% of functions accessible by voice
No reliance on visual-only or gesture-only features

Step 6: Contextual Help and Tutorials

User says: "Help" or "What can you do?"
System provides audio tutorial:
- Lists common commands
- Explains key features
- Offers to guide user through task
Tutorial should be:
- Clear and well-paced
- User can interrupt: "Skip" or "Next"
- Covers essential features in ≤3 minutes

Step 7: First-Time User Onboarding

New blind user performs initial setup:
- System provides audio-guided setup
- No visual cues required
- User can complete setup independently
Setup includes:
- Voice training (optional)
- Emergency contact setup
- Basic feature tutorial
Verify:
- User successfully completes setup
- Setup time ≤15 minutes
- User feels confident using device

Step 8: Long-Term Usability Assessment

Users use GF-VI for 1 week in daily life
Daily diary: log challenges and successes
End-of-week survey:
- "How often did accessibility features work as expected?" (%)
- "Did you encounter any tasks you couldn't complete?" (list)
- "Overall satisfaction with accessibility" (1-5 scale)
- "Would you recommend to other blind/low-vision users?" (Yes/No)
Target:
- Features work as expected ≥90% of time
- No critical tasks impossible to complete
- Satisfaction ≥4.0/5.0
- ≥85% would recommend

Pass Criteria

✅ Voice command recognition ≥95%
✅ Response latency ≤1 second
✅ Audio feedback provided for all actions
✅ User can navigate all menus without visual aid
✅ Error messages clear and actionable
✅ 100% of functions accessible by voice
✅ Contextual help clear and useful
✅ First-time setup completable independently ≤15 min
✅ Long-term: features work ≥90% of time
✅ Satisfaction ≥4.0/5.0
✅ ≥85% would recommend

Failure Modes to Monitor

Poor voice recognition
Delayed or missing audio feedback
Menu navigation confusing or impossible
Unclear error messages
Functions requiring visual interaction
Inadequate help/tutorial
Setup too difficult for independent completion

7. GF-TR: Traveller Edition Variant

Target User: International travelers, digital nomads, frequent flyers

TC-VAR-TR-001: Real-Time Translation Accuracy and Latency

Priority: P1 - Critical
Test Type: AI - Translation
Estimated Duration: 4 hours
Prerequisites: GF-TR variant with 24+ language packs

Objective

Validate that real-time translation provides accurate, natural, and timely translations across multiple languages in real-world travel scenarios.

Test Equipment

GF-TR variant
Native speakers of 6+ test languages
Standard translation benchmarks (WMT, FLORES)
Real-world travel scenarios
Audio recording equipment

Test Procedure

Step 1: Text Translation Accuracy (Written)

Test written translation across 24 language pairs:
- English ↔ Spanish, French, German, Italian, Portuguese
- English ↔ Japanese, Korean, Chinese (Simplified/Traditional)
- English ↔ Arabic, Hindi, Thai, Vietnamese
Use 500 sentence test set per language pair
Measure:
- BLEU score ≥30 (adequate quality)
- Human evaluation: fluency ≥4.0/5.0
- Human evaluation: adequacy ≥4.0/5.0
Compare against Google Translate baseline

Step 2: Speech-to-Speech Translation Latency

Native speaker says sentence in source language
System translates and speaks in target language
Measure end-to-end latency:
- Short sentences (≤10 words): ≤3 seconds
- Medium sentences (10-20 words): ≤5 seconds
- Long sentences (20+ words): ≤8 seconds
Test across 12 language pairs

Step 3: Conversation Mode (Two-Way Real-Time)

Two people have conversation:
- Person A speaks Language 1
- System translates to Language 2 for Person B
- Person B responds in Language 2
- System translates to Language 1 for Person A
Test 10-minute conversations across 6 language pairs
Measure:
- Translation accuracy ≥90% (human evaluation)
- Latency allows natural conversation flow
- Users report conversation felt "natural" ≥3.5/5.0
No awkward pauses or excessive delays

Step 4: Noisy Environment Robustness

Test translation in noisy travel environments:
- Busy street (70-80 dB background noise)
- Crowded restaurant (75-85 dB)
- Train station (80-90 dB)
- Airport terminal (70-80 dB)
Measure:
- STT WER increases ≤10% vs quiet
- Translation quality maintained
- User can understand translations clearly

Step 5: Accent and Dialect Handling

Test with varied accents within same language:
- Spanish: Spain vs Mexico vs Argentina
- English: US vs UK vs Australian
- French: France vs Quebec
- Arabic: Egyptian vs Gulf vs Levantine
Verify system handles accents adequately
Translation quality degradation ≤10% vs standard accent

Step 6: Domain-Specific Vocabulary

Test travel-specific phrases:
- "Where is the nearest ATM?"
- "I have a food allergy to peanuts"
- "How much does this cost?"
- "Where is the bathroom?"
- "I need a taxi to the airport"
- "Can I have the bill, please?"
Verify translations accurate for all travel phrases
Critical phrases (medical, safety) must be 100% accurate

Step 7: Offline Translation Performance

Test offline language packs:
- 6 most popular languages available offline
- User downloads before trip
Measure:
- Offline translation accuracy within 5% of online
- Latency comparable to online mode
- Language pack size ≤500 MB each

Step 8: Real-World Travel Scenario Testing

Users travel to foreign country (3-5 users, 3-5 days each)
Use GF-TR for all interactions:
- Hotel check-in
- Ordering food at restaurant
- Asking for directions
- Shopping and price negotiation
- Emergency communication (if needed)
Daily diary: record all translation uses
End-of-trip survey:
- "How often were translations accurate?" (%)
- "Did translation help you communicate effectively?" (1-5)
- "Would you rely on this for future travel?" (Yes/No)
Target:
- Translation accuracy ≥85% in real-world use
- Effective communication rating ≥4.0/5.0
- ≥90% would rely on this for future travel

Pass Criteria

✅ Written translation BLEU ≥30
✅ Human evaluation fluency and adequacy ≥4.0/5.0
✅ S2S latency: ≤3 sec (short), ≤5 sec (medium), ≤8 sec (long)
✅ Conversation mode accuracy ≥90%
✅ Conversation feels natural ≥3.5/5.0
✅ Noisy environment WER increase ≤10%
✅ Accent handling quality degradation ≤10%
✅ Travel phrase accuracy 100% for critical phrases
✅ Offline accuracy within 5% of online
✅ Real-world accuracy ≥85%
✅ Effective communication ≥4.0/5.0
✅ ≥90% would rely for future travel

Failure Modes to Monitor

Low BLEU scores or poor human evaluation
Excessive latency disrupting conversation
Poor handling of noisy environments
Accent recognition failures
Critical phrase mistranslations (medical, safety)
Offline mode significantly worse than online
Low user confidence in real-world scenarios

Priority: P1 - Critical
Test Type: AI - OCR + Translation
Estimated Duration: 3 hours
Prerequisites: GF-TR variant, camera, OCR + translation AI

Objective

Validate that the system can accurately read foreign-language text from signs, menus, and documents, then translate it to the user's language in real-time.

Test Equipment

GF-TR variant
Sample materials in 6+ foreign languages:
- Street signs
- Restaurant menus
- Product labels
- Information plaques
- Transportation schedules
Ground truth translations

Test Procedure

Step 1: Sign Translation Accuracy

User points camera at 30 foreign-language signs:
- Street signs (10)
- Store signs (5)
- Direction signs (5)
- Information boards (5)
- Safety signs (5)
System performs:
- OCR text extraction
- Language detection
- Translation to user's language
- Display translation as HUD overlay
Measure:
- OCR accuracy ≥95%
- Translation accuracy ≥90%
- End-to-end time ≤3 seconds
Test with 6+ languages

Step 2: Restaurant Menu Translation

User scans restaurant menu (3-5 pages)
System translates entire menu with:
- Dish names
- Descriptions
- Ingredients
- Prices
Verify:
- All menu items translated
- Allergy information preserved
- Prices correctly displayed in local currency
- Translation maintains context (e.g., "spicy")

Step 3: Real-Time Overlay Translation

User activates "live translation mode"
User points camera at foreign text
System overlays translation in real-time on HUD
As user moves camera, translation updates continuously
Verify:
- Translation overlays aligned with original text
- Updates smoothly (≥10 FPS)
- Readable and not obstructing view
- User can understand signs while walking

Step 4: Handwritten Text Translation

Test with handwritten notes and signs
More challenging than printed text
Verify:
- System attempts handwriting recognition
- If uncertain, informs user: "Handwritten text may be less accurate"
- Accuracy ≥70% for clear handwriting
- User can retry if needed

Step 5: Multi-Language Sign Translation

Test signs with multiple languages (common in tourist areas)
System should:
- Detect all languages present
- Translate each section appropriately
- Indicate which language each section is in
Example: Sign in English, Chinese, Japanese
User selects target language for translation

Step 6: Offline Menu Translation

Test offline translation of signs and menus
Verify:
- Offline packs for 6 popular languages available
- OCR works offline
- Translation works offline
- Accuracy within 10% of online mode

Step 7: Real-World Travel Usability

Users navigate foreign city using sign translation:
- Find specific restaurant
- Navigate metro system
- Read museum information plaques
- Understand store hours and policies
Survey:
- "Did sign translation help you navigate effectively?" (1-5)
- "Were translations accurate enough to be useful?" (%)
- "Would you rely on this feature when traveling?" (Yes/No)
Target:
- Navigation help ≥4.0/5.0
- Useful accuracy ≥85%
- ≥85% would rely on feature

Pass Criteria

✅ OCR accuracy ≥95%
✅ Translation accuracy ≥90%
✅ End-to-end time ≤3 seconds
✅ Menu translation complete and contextual
✅ Real-time overlay smooth (≥10 FPS)
✅ Handwriting recognition ≥70% (clear writing)
✅ Multi-language signs handled correctly
✅ Offline accuracy within 10% of online
✅ Navigation help ≥4.0/5.0
✅ Useful accuracy ≥85%
✅ ≥85% would rely on feature

Failure Modes to Monitor

Poor OCR in varied lighting or angles
Mistranslations leading to confusion
Slow translation disrupting real-time use
Handwriting recognition failures
Multi-language signs confused or mixed up
Offline mode significantly degraded
Users don't trust accuracy

TC-VAR-TR-003: Currency Conversion and Price Comparison

Priority: P2 - High
Test Type: Feature - Utility
Estimated Duration: 2 hours
Prerequisites: GF-TR variant, currency data API

Objective

Validate that the currency conversion feature provides accurate, up-to-date exchange rates and helps travelers quickly understand prices in their home currency.

Test Equipment

GF-TR variant
Price tags and menus in foreign currencies
Ground truth exchange rates from financial APIs
Test participants (3-5 travelers)

Test Procedure

Step 1: Real-Time Currency Conversion Accuracy

User points camera at foreign price tag
System:
- Detects price and currency via OCR
- Converts to user's home currency
- Displays conversion on HUD
Test with 30 prices in 10 different currencies
Measure:
- Price detection accuracy ≥95%
- Currency symbol recognition ≥98%
- Exchange rate accuracy ±1% of real-time rate

Step 2: Exchange Rate Update Frequency

Verify exchange rates updated regularly:
- Online mode: every 1 hour
- Offline mode: last update timestamp shown
User can manually refresh: "Update exchange rates"
System indicates: "Rates updated 2 hours ago"

Step 3: Multiple Price Comparison

User scans multiple items/options:
- Restaurant menu: compare dish prices
- Store: compare product prices
- Hotel: compare room rates
System highlights:
- Cheapest option
- Price differences in home currency
User can make informed decisions quickly

Step 4: Tip and Tax Calculation

User views bill total in foreign currency
System can calculate:
- Total with tax (if not included)
- Tip recommendation (15%, 18%, 20%)
- Final amount to pay
Verify calculations accurate
User can customize tip percentage

Step 5: Budget Tracking

User sets daily travel budget in home currency
As user makes purchases, system tracks spending:
- "You've spent $85 of your $150 daily budget"
Budget alerts:
- 75% spent: "You're at 75% of your daily budget"
- 100% spent: "You've reached your daily budget"
User can view spending summary

Step 6: Offline Mode Currency Conversion

User downloads currency data before trip
Test offline conversion:
- Last known exchange rates used
- System indicates: "Using offline rates from [date]"
Verify offline conversion functional
Accuracy acceptable (based on last update)

Step 7: User Satisfaction and Real-World Utility

Travelers use feature during 3-5 day trip
Survey:
- "How often did you use currency conversion?" (times/day)
- "Did it help you make better purchasing decisions?" (Yes/No)
- "Were conversions accurate enough?" (1-5)
- "Would you use this feature on future trips?" (Yes/No)
Target:
- Used ≥5 times/day
- Helped decisions: ≥85% yes
- Accuracy rating ≥4.0/5.0
- Future use: ≥90% yes

Pass Criteria

✅ Price detection accuracy ≥95%
✅ Currency recognition ≥98%
✅ Exchange rate accuracy ±1%
✅ Rates updated every 1 hour online
✅ Price comparison functional
✅ Tip and tax calculations accurate
✅ Budget tracking functional
✅ Offline conversion works (with timestamp)
✅ Usage ≥5 times/day
✅ Helped decisions ≥85%
✅ Accuracy rating ≥4.0/5.0
✅ Future use ≥90%

Failure Modes to Monitor

Inaccurate price detection
Wrong currency symbol recognition
Outdated exchange rates
Price comparison confusing or incorrect
Calculation errors
Budget tracking doesn't work
Offline mode non-functional

Priority: P1 - Critical
Test Type: Feature - Navigation
Estimated Duration: 3 hours
Prerequisites: GF-TR variant, offline map data

Objective

Validate that offline maps and navigation provide reliable guidance when traveling without internet connectivity.

Test Equipment

GF-TR variant
Offline map data for 3 test cities
Test routes (urban and suburban)
GPS accuracy testing equipment
Airplane mode / network simulator

Test Procedure

Step 1: Offline Map Download and Storage

User downloads offline map data for 3 cities:
- Small city: ≤50 km² (e.g., ≤100 MB)
- Medium city: 100-200 km² (e.g., ≤300 MB)
- Large city: ≥500 km² (e.g., ≤800 MB)
Verify:
- Download completes successfully
- Storage space required as advertised
- Maps accessible offline
User can manage downloaded maps:
- View list of downloaded maps
- Delete maps to free storage

Step 2: Offline Turn-by-Turn Navigation

Device in airplane mode (no internet)
User requests navigation to destination
System provides turn-by-turn guidance:
- Voice instructions
- Visual route on HUD (if available)
- Distance and ETA
Test 5 routes per city (15 total)
Measure:
- Navigation works 100% offline
- Route accuracy (user reaches destination)
- Instruction timing appropriate

Step 3: Offline POI (Point of Interest) Search

User searches for nearby POIs offline:
- "Find nearest restaurant"
- "Where is the closest pharmacy?"
- "Show me ATMs nearby"
System searches offline map data
Verify:
- POIs found and displayed
- Distance and direction to each POI
- User can select and navigate to POI

Step 4: Real-Time Position Tracking (GPS Only)

With offline maps and GPS only (no data):
- System tracks user position on map
- Updates position in real-time
Verify:
- GPS lock ≤30 seconds
- Position accuracy ±10 meters (urban)
- Position updates smoothly (≥1 Hz)
Test in varied environments:
- Open sky (best GPS)
- Urban canyon (buildings)
- Indoor (GPS weak/lost)

Step 5: Route Recalculation Offline

User navigation to destination
User intentionally misses turn or goes off-route
System recalculates route offline
Verify:
- Recalculation ≤10 seconds
- New route valid and efficient
- User guided back on track

Step 6: Offline Map Data Freshness

System indicates age of offline map data:
- "Map data from March 2025"
User can check for updates when online:
- "Update available for Paris map (102 MB)"
User decides whether to update
Old maps still functional

Step 7: Real-World Offline Navigation Test

Travelers use offline navigation in foreign city (no SIM/WiFi)
Navigate to 3-5 destinations per day for 3 days
Survey:
- "Did offline navigation work reliably?" (%)
- "Were you able to reach destinations without issues?" (%)
- "How confident did you feel using offline maps?" (1-5)
- "Would you rely on this when traveling?" (Yes/No)
Target:
- Reliability ≥95%
- Reached destinations ≥95%
- Confidence ≥4.0/5.0
- Would rely: ≥85%

Pass Criteria

✅ Offline maps download successfully
✅ Storage usage as advertised
✅ Navigation works 100% offline
✅ User reaches destination successfully
✅ Instruction timing appropriate
✅ Offline POI search functional
✅ GPS lock ≤30 seconds
✅ Position accuracy ±10 meters
✅ Route recalculation ≤10 seconds
✅ Map freshness indicated
✅ Real-world reliability ≥95%
✅ Confidence ≥4.0/5.0
✅ Would rely ≥85%

Failure Modes to Monitor

Download failures or corruption
Navigation doesn't work offline
Wrong instructions or routes
POI search missing or inaccurate
GPS lock slow or fails
No route recalculation offline
Map data outdated without indication

TC-VAR-TR-005: Cultural Context and Travel Assistant AI

Priority: P2 - High
Test Type: AI - Conversational
Estimated Duration: 3 hours
Prerequisites: GF-TR variant, travel AI assistant

Objective

Validate that the AI travel assistant provides helpful, culturally appropriate advice and recommendations for travelers in foreign destinations.

Test Equipment

GF-TR variant
Test scenarios for 5 different countries/cultures
Test participants (5 travelers with varied experience levels)
Cultural accuracy review panel

Test Procedure

Step 1: Cultural Etiquette Guidance

User asks about cultural customs:
- "What are local customs in Japan?"
- "Is tipping expected in France?"
- "How should I dress in Morocco?"
- "What gestures are offensive here?"
AI provides culturally appropriate advice
Verify:
- Information accurate (reviewed by cultural experts)
- Advice practical and specific
- Covers safety and respect considerations
Test across 10+ cultural contexts

Step 2: Local Recommendations

User asks for recommendations:
- "Where should I eat dinner?"
- "What should I see in this city?"
- "Any local festivals happening?"
- "Best area to stay in?"
AI considers:
- User location
- User preferences (if known)
- Local ratings and popularity
- Safety considerations
Verify recommendations relevant and helpful

Step 3: Emergency and Safety Information

User asks safety-related questions:
- "Is this area safe at night?"
- "What should I do if I lose my passport?"
- "Emergency number in this country?"
- "Nearest hospital or police station?"
AI provides:
- Accurate emergency numbers
- Clear safety advice
- Locations of embassies, hospitals, police
Safety information must be 100% accurate

Step 4: Conversational Travel Assistance

User has natural conversation with AI:
- "I'm hungry, what are some good local dishes?"
- "I have 3 hours before my flight, what can I do nearby?"
- "Can you help me plan tomorrow's itinerary?"
AI engages naturally and provides useful suggestions
Verify:
- Responses relevant to context
- Suggestions feasible given time/location
- Tone friendly and helpful (not robotic)

Step 5: Transportation Guidance

User asks about local transportation:
- "How do I get to the airport?"
- "Which metro line should I take?"
- "How much should a taxi cost?"
- "Is Uber available here?"
AI provides:
- Transportation options
- Estimated costs and times
- Tips (e.g., "Metro is faster than taxi during rush hour")
Information accurate and up-to-date

Step 6: Language Assistance

User asks: "How do I say [phrase] in local language?"
AI provides:
- Translation
- Pronunciation guide (phonetic)
- Audio of correct pronunciation (TTS)
Common phrases:
- Greetings (hello, goodbye, thank you)
- Asking for help ("Do you speak English?")
- Ordering food
- Basic numbers

Step 7: Real-World Travel AI Usability

Travelers use AI assistant during 3-5 day trip
Ask ≥10 questions per day across varied topics
Daily diary: record which questions were helpful
Survey:
- "How often were AI recommendations useful?" (%)
- "Did AI help you navigate cultural differences?" (1-5)
- "Would you use AI assistant on future trips?" (Yes/No)
- "Overall satisfaction with travel AI" (1-5)
Target:
- Recommendations useful ≥75%
- Cultural help ≥4.0/5.0
- Future use ≥80%
- Satisfaction ≥4.0/5.0

Pass Criteria

✅ Cultural information accurate (expert review)
✅ Advice practical and specific
✅ Recommendations relevant and helpful
✅ Safety information 100% accurate
✅ Conversational responses natural and useful
✅ Transportation guidance accurate
✅ Language assistance functional with pronunciation
✅ Real-world recommendations useful ≥75%
✅ Cultural help ≥4.0/5.0
✅ Future use ≥80%
✅ Satisfaction ≥4.0/5.0

Failure Modes to Monitor

Inaccurate or outdated cultural information
Culturally inappropriate or offensive advice
Incorrect safety or emergency information
Unhelpful or generic recommendations
Poor conversational quality
Wrong transportation guidance
Language assistance inaccurate

8. GF-LX: Lifestyle Edition Variant

Target User: Consumer wellness market, general AI assistant users

TC-VAR-LX-001: Personal Journaling and Daily Reflection

Priority: P2 - High
Test Type: Feature - AI Assistant
Estimated Duration: 2 hours (+ 1 week user testing)
Prerequisites: GF-LX variant, journaling feature enabled

Objective

Validate that the AI-assisted journaling feature helps users capture thoughts, track mood, and reflect on their day in a meaningful and private way.

Test Equipment

GF-LX variant
Test participants (5+ users)
1-week user testing period
Survey instruments

Test Procedure

Step 1: Voice-to-Journal Entry

User says: "Start journal entry" or "I want to journal"
AI prompts: "How are you feeling today?"
User speaks naturally about their day
AI:
- Transcribes speech (Whisper STT)
- Adds timestamps
- Structures entry into paragraphs
- Saves encrypted locally
Verify:
- Transcription accuracy ≥95%
- Entry saved successfully
- User can review entry

Step 2: AI Journal Prompts and Reflection Questions

AI provides thoughtful prompts:
- "What made you smile today?"
- "What challenged you this week?"
- "What are you grateful for?"
- "What's on your mind?"
Prompts should:
- Be varied (not repetitive)
- Encourage reflection
- Adapt to user's mood and context
User can accept prompt or free-form journal

Step 3: Mood Tracking and Trends

At end of journal entry, AI asks: "How would you rate your mood today?" (1-5 scale)
AI tracks mood over time:
- Daily mood scores
- Weekly/monthly averages
- Trends visualization (if HUD supports)
Verify:
- Mood data saved correctly
- User can view mood history
- Trends identified: "Your mood has been improving this week"

Step 4: Journal Search and Reflection

User says: "Show me my entries about [topic]"
AI searches journal entries
Returns relevant entries:
- Snippets with highlights
- User can read full entry
Verify:
- Search accuracy ≥90%
- Results ranked by relevance
- User can find past entries easily

Step 5: Privacy and Security

All journal entries encrypted at rest
User can set password/PIN for journal access
AI never shares journal content externally
User can export or delete all journal data
Verify:
- Encryption functional (AES-256)
- Password protection works
- Export to plain text/PDF functional
- Deletion removes all data

Step 6: AI Insights and Patterns

After 1+ week of journaling, AI provides insights:
- "I noticed you mention work stress frequently"
- "Your mood tends to improve after exercise"
- "You've been sleeping well this week"
Insights should:
- Be based on journal content only
- Be non-judgmental and supportive
- Respect privacy (no external data)
User finds insights helpful ≥3.5/5.0

Step 7: Long-Term User Adoption (1 Week)

Users journal daily for 1 week
Track:
- Journaling frequency (goal: 5+ days/week)
- Entry length (minutes of speech)
- User engagement with prompts
End-of-week survey:
- "Did journaling help you reflect on your day?" (1-5)
- "Were AI prompts helpful?" (1-5)
- "Do you feel more self-aware after journaling?" (1-5)
- "Will you continue journaling after the study?" (Yes/No)
Target:
- Reflection helpful ≥4.0/5.0
- Prompts helpful ≥3.5/5.0
- Self-awareness improved ≥3.5/5.0
- Continue use ≥60%

Pass Criteria

✅ Transcription accuracy ≥95%
✅ Journal entries saved correctly
✅ AI prompts varied and thoughtful
✅ Mood tracking functional
✅ Search accuracy ≥90%
✅ Encryption and privacy functional (AES-256)
✅ AI insights helpful ≥3.5/5.0
✅ Reflection helpful ≥4.0/5.0
✅ Prompts helpful ≥3.5/5.0
✅ Self-awareness ≥3.5/5.0
✅ Continue use ≥60%

Failure Modes to Monitor

Poor transcription quality
Entries not saved or corrupted
Repetitive or unhelpful prompts
Mood tracking inaccurate
Search doesn't find relevant entries
Privacy/encryption failures
AI insights not helpful or creepy

TC-VAR-LX-002: Wellness Reminders and Habit Formation

Priority: P2 - High
Test Type: Feature - Behavior Change
Estimated Duration: 1 week per user
Prerequisites: GF-LX variant, habit tracking enabled

Objective

Validate that the system helps users build healthy habits through gentle reminders, progress tracking, and positive reinforcement without being annoying or intrusive.

Test Equipment

GF-LX variant
Test participants (5+ users)
1-week habit formation period
Daily check-in surveys

Test Procedure

Step 1: Habit Setup and Goal Setting

User selects habits to build:
- Drink water (8 glasses/day)
- Take breaks (every 2 hours)
- Exercise (30 min, 3×/week)
- Meditation (10 min/day)
- Sleep by 11 PM
AI helps user set realistic goals:
- "How often would you like to meditate?"
- "What time works best for you?"
User can customize:
- Reminder frequency
- Reminder time
- Notification style (audio, HUD, haptic)

Step 2: Gentle Reminder Delivery

System delivers reminders at set times:
- "It's been 2 hours, time to take a break"
- "Have you had water recently?"
- "Evening reminder: meditation session"
Verify:
- Reminders delivered on schedule
- Reminder tone calm and encouraging (not nagging)
- User can snooze or dismiss
User rates reminder appropriateness (1-5)

Step 3: Habit Tracking and Check-Ins

After reminder, AI asks: "Did you complete [habit]?"
User confirms: Yes / No / Snooze
System tracks completion:
- Daily completion rate
- Weekly streak
- Total completions
Verify:
- Tracking accurate
- User can view progress easily
- Missed days recorded appropriately

Step 4: Positive Reinforcement

When user completes habit:
- "Great job! You've meditated 3 days in a row"
- "You're on a 5-day water intake streak!"
Reinforcement should:
- Be genuine and personalized
- Celebrate progress (not perfection)
- Encourage without pressure
User finds reinforcement motivating ≥3.5/5.0

Step 5: Adaptive Reminder Timing

System learns optimal reminder times:
- If user often snoozes morning reminders → shift to afternoon
- If user never completes evening habits → suggest earlier time
After 1 week, AI suggests adjustments:
- "I notice you usually exercise around 6 PM. Want to set reminder then?"
Verify adaptation improves completion rate

Step 6: No Guilt or Pressure

When user misses habit:
- "That's okay, try again tomorrow"
- NO: "You missed your goal" or "You failed"
System focuses on progress, not perfection
User can pause habits without penalty:
- "I'm on vacation, pause all reminders"
User reports feeling: supportive (not guilty) ≥4.0/5.0

Step 7: Long-Term Habit Formation (1 Week)

Users track 2-3 habits for 1 week
Daily surveys:
- "Did reminders help you remember?" (Yes/No)
- "Were reminders too frequent?" (Yes/No)
- "Are you building the habit?" (1-5)
End-of-week survey:
- "Habit completion rate improved?" (%)
- "Do you feel more mindful of habits?" (1-5)
- "Will you continue using habit tracking?" (Yes/No)
Target:
- Reminders helpful ≥80% of days
- Not too frequent ≥85% feel reminders appropriate
- Habit building ≥3.5/5.0
- Continue use ≥65%

Pass Criteria

✅ Users can set up habits easily
✅ Reminders delivered on schedule
✅ Reminder tone appropriate (not nagging)
✅ Tracking accurate
✅ Positive reinforcement motivating ≥3.5/5.0
✅ Adaptive timing improves completion
✅ User feels supported, not guilty ≥4.0/5.0
✅ Reminders helpful ≥80% of days
✅ Frequency appropriate ≥85%
✅ Habit building ≥3.5/5.0
✅ Continue use ≥65%

Failure Modes to Monitor

Reminders not delivered on time
Reminders annoying or too frequent
Tracking inaccurate or confusing
Reinforcement feels fake or robotic
System makes user feel guilty
Adaptive timing doesn't improve results

TC-VAR-LX-003: Conversational AI Companion Quality

Priority: P1 - Critical
Test Type: AI - Conversational
Estimated Duration: 1 week per user
Prerequisites: GF-LX variant, conversational AI enabled

Objective

Validate that the AI companion provides natural, helpful, and emotionally appropriate conversations that enhance the user's lifestyle and well-being.

Test Equipment

GF-LX variant
Test participants (5+ users with varied needs)
1-week continuous use period
Conversation quality rating tools

Test Procedure

Step 1: Natural Conversation Flow

Users have 10+ conversations with AI per day for 1 week
Topics:
- Small talk and daily check-ins
- Planning and reminders
- Information queries
- Problem-solving and advice
- Emotional support
Record and analyze 50 conversations per user
Measure:
- Conversation coherence (1-5): ≥4.0
- Response relevance (1-5): ≥4.0
- Turn-taking naturalness (1-5): ≥3.8

Step 2: Contextual Awareness

AI remembers context from previous conversations:
- User mentions "going to dentist tomorrow"
- Next day, AI asks: "How was your dentist appointment?"
AI uses location/time context appropriately:
- Morning: "Good morning, how did you sleep?"
- At gym: "Ready for your workout?"
Verify:
- Context recall ≥85% of time
- Contextual responses feel natural
- No creepy or invasive context usage

Step 3: Emotional Tone Matching

AI detects user's emotional state via:
- Voice tone (pace, pitch, volume)
- Word choice
- Conversation history
AI adapts tone:
- User stressed → calming, supportive tone
- User excited → enthusiastic tone
- User sad → empathetic tone
Verify:
- Tone matching feels appropriate ≥85%
- Never dismissive or invalidating
- User feels "understood" ≥4.0/5.0

Step 4: Helpful Without Overstepping

AI provides suggestions and advice when appropriate
But respects boundaries:
- Doesn't push unsolicited advice
- Doesn't make assumptions about personal matters
- Knows when to say "I'm not sure, maybe consult [expert]"
User feedback:
- "AI respects my boundaries" ≥4.5/5.0
- "AI is helpful but not pushy" ≥4.0/5.0

Step 5: Avoiding Over-Dependence

AI should enhance life, not replace human connections
AI periodically encourages:
- Reaching out to friends/family
- Engaging in real-world activities
- Taking breaks from screens
AI does NOT:
- Encourage exclusive relationship with AI
- Discourage human interaction
- Claim to be human or have feelings
User understands AI limitations ≥90%

Step 6: Conversation Quality Metrics

Analyze 50 conversations per user
Human evaluation (3 independent raters):
- Coherence: maintains logical flow (1-5)
- Relevance: responses on-topic (1-5)
- Helpfulness: provides useful info/support (1-5)
- Appropriateness: tone and content suitable (1-5)
- Naturalness: feels conversational (1-5)
Target all metrics ≥4.0/5.0

Step 7: Long-Term User Satisfaction (1 Week)

Daily check-ins:
- "Did AI provide helpful conversation today?" (Yes/No)
- "Did you enjoy talking with AI?" (1-5)
- "Any concerning or inappropriate responses?" (describe)
End-of-week survey:
- "Overall AI companion quality" (1-5)
- "Would you continue using AI companion?" (Yes/No)
- "Would you recommend to friends?" (Yes/No)
- "How does AI compare to other assistants?" (Better/Same/Worse)
Target:
- Helpful ≥75% of days
- Enjoyment ≥3.8/5.0
- Overall quality ≥4.0/5.0
- Continue use ≥70%
- Recommend ≥60%
- Better than other assistants ≥40%

Pass Criteria

✅ Conversation coherence ≥4.0/5.0
✅ Response relevance ≥4.0/5.0
✅ Turn-taking naturalness ≥3.8/5.0
✅ Context recall ≥85%
✅ Emotional tone matching appropriate ≥85%
✅ User feels understood ≥4.0/5.0
✅ Respects boundaries ≥4.5/5.0
✅ Helpful but not pushy ≥4.0/5.0
✅ Does not encourage over-dependence
✅ Human evaluation all metrics ≥4.0/5.0
✅ Helpful conversation ≥75% of days
✅ Enjoyment ≥3.8/5.0
✅ Overall quality ≥4.0/5.0
✅ Continue use ≥70%

Failure Modes to Monitor

Incoherent or irrelevant responses
Poor context recall
Inappropriate emotional tone
Overstepping boundaries
Encouraging over-dependence
Robotic or unnatural conversation
User dissatisfaction or concerns

TC-VAR-LX-004: Smart Home Integration (Basic)

Priority: P3 - Medium
Test Type: Integration - IoT
Estimated Duration: 2 hours
Prerequisites: GF-LX variant, compatible smart home devices

Objective

Validate basic smart home control via voice commands through GROOT FORCE glasses for common devices and scenarios.

Test Equipment

GF-LX variant
Smart home test setup:
- Smart lights (Philips Hue or similar)
- Smart plug
- Smart speaker
- Smart thermostat (if available)
Integration platform (Google Home, Alexa, or native)

Test Procedure

Step 1: Device Discovery and Pairing

User initiates: "Setup smart home devices"
System discovers nearby compatible devices
User authenticates with smart home platform (Google/Alexa account)
Verify:
- Device discovery finds ≥90% of devices
- Pairing completes successfully
- Devices controllable via AI

Step 2: Basic Device Control Commands

User gives voice commands:
- "Turn on living room lights"
- "Set bedroom lights to 50%"
- "Turn off all lights"
- "Set thermostat to 22 degrees"
- "Turn on fan"
Verify:
- Commands recognized correctly ≥95%
- Actions execute within 2 seconds
- Devices respond as expected
- Audio confirmation provided

Step 3: Scenes and Routines

User can trigger smart home scenes:
- "Good morning" → lights on, open blinds, play music
- "Movie time" → dim lights, TV on
- "Goodnight" → all lights off, lock doors, arm security
Verify:
- Scenes trigger multiple actions correctly
- All scene actions complete successfully
- User can create custom scenes (if supported)

Step 4: Status Queries

User asks device status:
- "Are the bedroom lights on?"
- "What's the temperature set to?"
- "Is the front door locked?"
AI queries devices and responds:
- "Bedroom lights are currently off"
- "Thermostat is set to 22 degrees, current temp is 21"
Verify status queries accurate ≥95%

Step 5: Multi-Device Control

User controls multiple devices simultaneously:
- "Turn off all lights except the kitchen"
- "Set all upstairs lights to 70%"
Verify:
- Multi-device commands work correctly
- Exclusions handled properly
- All specified devices respond

Step 6: Error Handling

Simulate device failures:
- Device offline
- Command not supported
- Platform authentication expired
Verify:
- Clear error messages
- User knows what went wrong
- Recovery suggestions provided

Step 7: User Satisfaction

Users control smart home via GF-LX for 1 week
Survey:
- "Was voice control convenient?" (1-5)
- "Did commands work reliably?" (%)
- "Would you prefer this over phone/app?" (Yes/No)
- "Overall satisfaction with smart home control" (1-5)
Target:
- Convenience ≥4.0/5.0
- Reliability ≥90%
- Prefer over phone ≥50%
- Satisfaction ≥3.8/5.0

Pass Criteria

✅ Device discovery ≥90%
✅ Pairing successful
✅ Commands recognized ≥95%
✅ Actions execute ≤2 seconds
✅ Scenes trigger correctly
✅ Status queries accurate ≥95%
✅ Multi-device commands work
✅ Error messages clear
✅ Convenience ≥4.0/5.0
✅ Reliability ≥90%
✅ Satisfaction ≥3.8/5.0

Failure Modes to Monitor

Device discovery incomplete
Pairing failures
Commands misrecognized or fail to execute
Scenes don't trigger all actions
Status queries inaccurate
Multi-device control errors
Poor error handling

Priority: P2 - High
Test Type: Feature - Camera + Social
Estimated Duration: 2 hours
Prerequisites: GF-LX variant, social media accounts

Objective

Validate that users can easily capture photos/videos hands-free and share them to social media platforms quickly and seamlessly.

Test Equipment

GF-LX variant
Test social media accounts (Instagram, Facebook, TikTok)
Various content scenarios

Test Procedure

Step 1: Hands-Free Photo Capture

User says: "Take a photo" or "Capture"
System:
- Shows camera preview on HUD (if available)
- Counts down: "3, 2, 1" or beep
- Captures photo
- Confirms: "Photo saved"
Verify:
- Voice command recognition ≥98%
- Photo quality adequate (12+ MP)
- Proper exposure and focus
- Saved successfully to device

Step 2: Hands-Free Video Recording

User says: "Start recording" or "Record video"
System:
- Begins recording
- Shows recording indicator (red dot HUD + LED)
User says: "Stop recording"
System:
- Stops recording
- Saves video
- Confirms: "Video saved, 42 seconds"
Verify:
- Start/stop commands work reliably ≥98%
- Video quality 1080p or better
- Stabilization functional
- Audio clear

Step 3: Quick Review and Delete

User says: "Show last photo" or "Review"
System displays photo on HUD or sends to phone
User can:
- "Delete" or "Keep"
- "Retake"
Verify quick review functional

Step 4: AI Caption Generation

After capturing content, AI offers:
- "Would you like me to suggest a caption?"
AI generates caption based on image content:
- "Beautiful sunset over the ocean 🌅"
- "Coffee and coding ☕💻"
User can:
- Accept caption
- Modify caption
- Skip caption
Caption quality: relevant and appropriate ≥80%

Step 5: Social Media Sharing

User says: "Share to Instagram" or "Post to Facebook"
System:
- Opens sharing interface (phone app or direct API)
- Pre-fills caption if generated
- User reviews and confirms
Content posted successfully
Verify:
- Sharing works for ≥3 platforms (Instagram, Facebook, TikTok, Twitter/X)
- Post appears correctly on platform
- Image quality maintained
- Hashtags and tags functional (if used)

Step 6: Privacy and Consent

System always shows clear indication when recording:
- Red LED on glasses (hardware-wired)
- HUD indicator
- Audio beep (if enabled)
Verify:
- Indicators cannot be disabled
- Bystanders can see recording is happening
- User can explain feature to others

Step 7: Content Organization

All captured content organized:
- By date/time
- By location (if GPS enabled)
- User can add tags
User can search content:
- "Show me photos from last week"
- "Find videos from Paris"
Search accuracy ≥85%

Step 8: User Satisfaction and Real-World Use

Users capture content for 1 week
Track:
- Photos/videos captured per day
- Share rate (% of content shared)
- User satisfaction with capture quality
Survey:
- "Was hands-free capture convenient?" (1-5)
- "Photo/video quality satisfactory?" (1-5)
- "Sharing process easy?" (1-5)
- "Would you use this regularly?" (Yes/No)
Target:
- Convenience ≥4.2/5.0
- Quality ≥4.0/5.0
- Sharing ease ≥4.0/5.0
- Regular use ≥60%

Pass Criteria

✅ Photo capture command ≥98% accurate
✅ Photo quality adequate (12+ MP)
✅ Video start/stop reliable ≥98%
✅ Video quality 1080p+ with stabilization
✅ Quick review functional
✅ AI captions relevant ≥80%
✅ Social sharing works for ≥3 platforms
✅ Privacy indicators cannot be disabled
✅ Content organization and search functional
✅ Convenience ≥4.2/5.0
✅ Quality ≥4.0/5.0
✅ Sharing ease ≥4.0/5.0
✅ Regular use ≥60%

Failure Modes to Monitor

Voice commands not recognized
Poor photo/video quality
Recording fails to start/stop
AI captions inappropriate or irrelevant
Sharing failures
Privacy indicators malfunction
Search doesn't find content
User dissatisfaction with feature

9. GF-EN: Enterprise Vision (Industrial Fleet) Variant

Target User: Industrial companies, fleet deployments, enterprise admins

TC-VAR-EN-001: SOP (Standard Operating Procedure) Integration

Priority: P1 - Critical
Test Type: Enterprise - Document Integration
Estimated Duration: 4 hours
Prerequisites: GF-EN variant, SOP management system access

Objective

Validate that workers can access, follow, and complete Standard Operating Procedures hands-free via GROOT FORCE, improving compliance and efficiency.

Test Equipment

GF-EN variant (3-5 units for multi-user testing)
Sample SOPs (10+ procedures across various complexity)
Industrial task simulation environment
Enterprise admin dashboard
Test workers (3-5 with varied experience levels)

Test Procedure

Step 1: SOP Upload and Management

Enterprise admin uploads SOPs via web dashboard:
- PDF documents
- Step-by-step procedures
- Images and diagrams
- Safety warnings
Admin can:
- Organize SOPs by department/task
- Set access permissions
- Version control SOPs
- Assign SOPs to specific workers
Verify:
- Upload completes successfully for all formats
- SOPs sync to devices within 5 minutes
- Organization structure reflected on device
- Access control enforced

Step 2: SOP Discovery and Access

Worker requests SOP:
- "Show me the forklift inspection procedure"
- "Open safety lockout SOP"
- "How do I calibrate the sensor?"
System:
- Searches SOP library
- Displays relevant SOP on HUD
- Reads step-by-step if requested
Verify:
- Search finds correct SOP ≥95% of time
- SOP displays clearly on HUD
- Worker can navigate SOP hands-free

Step 3: Step-by-Step Guided Execution

Worker follows SOP step-by-step:
- System displays current step on HUD
- Worker says "Next step" to advance
- Worker says "Previous step" or "Repeat" if needed
For each step:
- Text instruction displayed
- Relevant image/diagram shown (if available)
- Safety warnings highlighted
Verify:
- Worker can complete SOP hands-free
- Navigation intuitive (≤5 sec per step)
- All steps followed in correct order

Step 4: Safety Warning Escalation

Critical safety steps have special treatment:
- Red indicator on HUD
- Verbal warning: "Safety critical step"
- Requires explicit confirmation:
  - "I have verified the valve is closed"
  - System records confirmation
Verify:
- Safety warnings always displayed
- Worker cannot skip safety steps
- Confirmations recorded in audit log

Step 5: Photo Documentation of Steps

SOP requires photo evidence for steps:
- "Take photo of completed setup"
- Worker says "Capture" or "Take photo"
- Photo attached to SOP completion record
Verify:
- Photos captured easily
- Photos linked to correct step
- Photos included in completion report

Step 6: SOP Completion Tracking and Reporting

Upon SOP completion:
- System asks: "Mark SOP complete?"
- Worker confirms
- Completion record generated:
  - Worker ID
  - SOP version
  - Timestamp
  - Duration
  - Photo evidence
  - Safety confirmations
Admin dashboard shows:
- Which SOPs completed by which workers
- Completion rates
- Average time per SOP
- Outstanding/overdue SOPs
Verify:
- Completion tracking 100% accurate
- Dashboard updates within 5 minutes
- Reports exportable (CSV, PDF)

Step 7: Offline SOP Access

Worker in area without network connectivity
SOPs previously synced available offline
Verify:
- Offline SOPs fully functional
- All steps, images accessible
- Completion records sync when back online
- No data loss

Step 8: Real-World Industrial Task Testing

Workers perform 5 real SOPs using GF-EN
Tasks:
- Equipment inspection (10 steps)
- Maintenance procedure (15 steps)
- Safety audit (8 steps)
- Quality check (12 steps)
- Tool calibration (7 steps)
Measure:
- Task completion success rate ≥95%
- Time vs paper SOP (target: ≤110% of baseline)
- Error rate (skipped/incorrect steps) ≤3%
- Worker satisfaction ≥4.0/5.0

Step 9: Admin and Worker Satisfaction

Admin survey:
- "Easier to manage SOPs vs paper/tablets?" (1-5)
- "Improved compliance tracking?" (Yes/No)
- "Would you expand deployment?" (Yes/No)
Worker survey:
- "Hands-free SOP access helpful?" (1-5)
- "Easier than paper/tablet?" (1-5)
- "Felt safer following SOP?" (1-5)
- "Would you prefer this method?" (Yes/No)
Target:
- Admin ease ≥4.0/5.0
- Improved compliance ≥80% yes
- Expand deployment ≥75% yes
- Worker helpfulness ≥4.0/5.0
- Easier than alternatives ≥3.8/5.0
- Felt safer ≥4.2/5.0
- Prefer method ≥70%

Pass Criteria

✅ SOP upload all formats successful
✅ Sync to devices ≤5 minutes
✅ Access control enforced
✅ Search finds correct SOP ≥95%
✅ Hands-free navigation functional
✅ Safety warnings always displayed, cannot be skipped
✅ Photo documentation functional
✅ Completion tracking 100% accurate
✅ Dashboard updates ≤5 minutes
✅ Offline SOPs fully functional
✅ Task completion ≥95%
✅ Time ≤110% of baseline
✅ Error rate ≤3%
✅ Admin ease ≥4.0/5.0, improved compliance ≥80%
✅ Worker helpfulness ≥4.0/5.0, felt safer ≥4.2/5.0
✅ Prefer method ≥70%

Failure Modes to Monitor

SOP upload failures or corruption
Sync delays or failures
Incorrect SOPs delivered to workers
Safety warnings skipped or missed
Photo capture fails
Completion records lost
Offline mode doesn't work
High error rates in real tasks
Low worker or admin satisfaction

TC-VAR-EN-002: Remote Expert Assistance and Video Support

Priority: P1 - Critical
Test Type: Enterprise - Remote Collaboration
Estimated Duration: 3 hours
Prerequisites: GF-EN variant, remote expert platform, network connectivity

Objective

Validate that field workers can connect with remote experts for real-time guidance via live video, audio, and AR annotations, reducing downtime and improving first-time fix rates.

Test Equipment

GF-EN variant (field worker)
Remote expert station (desktop with software)
Test scenarios (troubleshooting, repair, inspection)
Network: 4G/5G or Wi-Fi

Test Procedure

Step 1: Initiating Remote Expert Session

Worker encounters problem requiring expert help
Worker says: "Connect to expert" or presses physical button
System:
- Notifies available experts
- Shows queue position if experts busy
- Establishes connection when expert accepts
Verify:
- Connection established ≤30 seconds (when expert available)
- Queue system functional
- Worker notified of connection status

Step 2: Two-Way Video Communication

Video stream from worker's camera to expert
Audio two-way communication
Expert sees what worker sees in real-time
Verify:
- Video quality adequate for diagnostics (≥720p, ≥15 FPS)
- Audio clear both directions
- Latency ≤500ms
- No frequent disconnections

Step 3: AR Annotations from Expert

Expert draws annotations on worker's view:
- "Turn that bolt" (arrow pointing to bolt)
- "Check this wire" (circle around wire)
- "Warning: high voltage" (text overlay)
Worker sees annotations on HUD in real-time
Verify:
- Annotations appear on worker's HUD ≤1 sec
- Annotations aligned with real-world objects
- Multiple annotation types supported (arrow, circle, text, highlight)
- Annotations clear and helpful

Step 4: Screen Sharing (Expert to Worker)

Expert shares screen with worker:
- Showing wiring diagram
- Displaying troubleshooting flowchart
- Showing parts catalog
Worker views shared content on HUD
Verify:
- Screen sharing quality adequate
- Worker can see details
- Worker can zoom or pan (if needed)

Step 5: Photo and Video Capture During Session

Expert requests: "Take a photo of that component"
Worker captures photo, visible to both parties
Expert can annotate photo and save to case file
Verify:
- Photo capture during call functional
- Both parties see photo
- Annotations saved with photo

Step 6: Session Recording and Documentation

Remote expert sessions automatically recorded (with consent)
Recording includes:
- Video from worker
- Audio both directions
- AR annotations
- Photos captured
Recording saved to case management system
Verify:
- Recording captures all elements
- Recording playable after session
- Recording linked to work order

Step 7: Network Resilience and Reconnection

Simulate network disruption during session:
- 4G/5G handoff
- Brief disconnection (10-30 sec)
- Low bandwidth period
Verify:
- System attempts reconnection automatically
- Worker notified of connection status
- Session resumes without data loss
- Graceful degradation (lower resolution) if bandwidth limited

Step 8: Real-World Troubleshooting Scenarios

Workers perform 5 real troubleshooting tasks with remote expert:
- Equipment malfunction (15 min)
- Quality issue diagnosis (10 min)
- Safety inspection (12 min)
- Complex repair (20 min)
- Calibration assistance (8 min)
Measure:
- First-time fix rate (with vs without remote expert)
- Time to resolution
- Worker confidence (1-5 scale)
- Expert effectiveness (1-5 scale)
Compare to baseline (phone call or no remote support)

Step 9: Worker and Expert Satisfaction

Worker survey (after 5+ remote sessions):
- "Video and AR annotations helped solve problem?" (1-5)
- "Faster than phone call or waiting for on-site expert?" (Yes/No)
- "System easy to use during task?" (1-5)
- "Would you use this regularly?" (Yes/No)
Expert survey:
- "Could diagnose problem effectively?" (1-5)
- "AR annotations useful?" (1-5)
- "Video quality adequate?" (1-5)
- "Prefer this vs phone call?" (Yes/No)
Target:
- Worker: problem solved ≥4.2/5.0, faster ≥80%, easy ≥4.0/5.0, regular use ≥85%
- Expert: diagnose ≥4.0/5.0, annotations useful ≥4.3/5.0, video quality ≥4.0/5.0, prefer ≥75%

Pass Criteria

✅ Connection established ≤30 seconds
✅ Video quality ≥720p, ≥15 FPS
✅ Audio clear both directions
✅ Latency ≤500ms
✅ AR annotations appear ≤1 sec, aligned correctly
✅ Screen sharing functional
✅ Photo capture during call works
✅ Session recording captures all elements
✅ Automatic reconnection functional
✅ Worker: problem solved ≥4.2/5.0, faster ≥80%, easy ≥4.0/5.0, regular use ≥85%
✅ Expert: diagnose ≥4.0/5.0, annotations useful ≥4.3/5.0, video ≥4.0/5.0, prefer ≥75%

Failure Modes to Monitor

Connection failures or long delays
Poor video/audio quality
AR annotations misaligned or delayed
Screen sharing doesn't work
Photo capture fails during call
Recording incomplete or corrupted
No automatic reconnection
Low satisfaction from workers or experts

TC-VAR-EN-003: Fleet Management and Device Provisioning

Priority: P1 - Critical
Test Type: Enterprise - IT Administration
Estimated Duration: 4 hours
Prerequisites: GF-EN variants (10+ units), enterprise admin dashboard

Objective

Validate that enterprise admins can efficiently provision, monitor, update, and manage a fleet of GROOT FORCE devices across distributed teams.

Test Equipment

GF-EN variants (10+ test units)
Enterprise admin dashboard (web-based)
Test user accounts (10+ workers)
MDM (Mobile Device Management) platform

Test Procedure

Step 1: Bulk Device Provisioning

Admin receives 10 new GF-EN devices
Admin provisions devices via dashboard:
- Create device profiles
- Assign to workers
- Configure settings (network, apps, permissions)
- Deploy SOPs and skills
Verify:
- Provisioning 10 devices ≤30 minutes
- All settings applied correctly
- Workers receive assigned devices with correct configs
- Zero-touch deployment functional (minimal user setup)

Step 2: User Account and Permission Management

Admin creates user accounts:
- Worker role (field access)
- Supervisor role (oversight + worker access)
- Admin role (full control)
Admin assigns permissions:
- Which SOPs each user can access
- Which features enabled (camera, remote expert, etc.)
- Data access controls
Verify:
- Permissions enforced on devices
- Users cannot access restricted features
- Role-based access control (RBAC) functional

Step 3: Remote Device Monitoring

Admin dashboard shows real-time fleet status:
- Online/offline devices
- Battery levels
- Location (if GPS enabled)
- Current task/SOP (if active)
- Storage usage
- Firmware version
Verify:
- Dashboard updates ≤5 minutes
- All devices visible
- Status information accurate

Step 4: Remote Configuration and Policy Updates

Admin updates device policies remotely:
- Change Wi-Fi settings
- Update app permissions
- Deploy new SOP
- Enable/disable features
Policies push to devices immediately (if online) or next sync
Verify:
- Policy updates received within 10 minutes (online devices)
- Offline devices update upon reconnection
- No user disruption during update

Step 5: Over-the-Air (OTA) Firmware Updates

Admin schedules firmware update:
- Select devices or groups
- Schedule update time (e.g., overnight)
- Rollout strategy: phased (10% → 50% → 100%)
Devices download and install update
Verify:
- Updates deploy successfully ≥98% of devices
- Phased rollout prevents mass failures
- Devices reboot and return to service
- No data loss during update
- Failed updates rollback automatically

Step 6: Device Health and Alerts

Admin receives alerts for:
- Device offline > 24 hours
- Low battery ( < 10%) for extended period
- Sensor failures
- High error rates
- Storage nearly full
Verify:
- Alerts delivered via email and dashboard
- Alerts accurate and actionable
- Admin can drill down into details

Step 7: Usage Analytics and Reporting

Admin views fleet analytics:
- SOPs completed per worker per day
- Average SOP completion time
- Most frequently used SOPs
- Remote expert session frequency and duration
- Photo/video capture volume
- Device uptime and reliability
Verify:
- Analytics accurate
- Reports exportable (CSV, PDF)
- Dashboards customizable

Step 8: Device Wiping and Decommissioning

Worker leaves company or device lost/stolen
Admin remotely wipes device:
- All user data erased
- Device returns to factory settings
- Device removed from fleet
Verify:
- Remote wipe completes successfully
- All data unrecoverable
- Device cannot be used until re-provisioned

Step 9: Enterprise IT Satisfaction

IT admin manages 10-device fleet for 1 week
Survey:
- "Provisioning process easy?" (1-5)
- "Dashboard provides needed visibility?" (1-5)
- "Remote management capabilities adequate?" (1-5)
- "Fleet management easier than tablets/phones?" (Better/Same/Worse)
- "Would you recommend for enterprise deployment?" (Yes/No)
Target:
- Provisioning ease ≥4.0/5.0
- Dashboard visibility ≥4.2/5.0
- Management capabilities ≥4.0/5.0
- Easier than alternatives ≥40%
- Recommend ≥75%

Pass Criteria

✅ Bulk provisioning 10 devices ≤30 minutes
✅ Zero-touch deployment functional
✅ RBAC enforced correctly
✅ Dashboard updates ≤5 minutes
✅ Status information accurate
✅ Policy updates delivered ≤10 minutes
✅ OTA updates successful ≥98%
✅ Failed updates rollback automatically
✅ Health alerts accurate and delivered
✅ Analytics accurate and exportable
✅ Remote wipe successful, data unrecoverable
✅ Provisioning ease ≥4.0/5.0
✅ Dashboard visibility ≥4.2/5.0
✅ Management capabilities ≥4.0/5.0
✅ Recommend ≥75%

Failure Modes to Monitor

Provisioning slow or fails
Settings not applied correctly
Permission enforcement failures
Dashboard outdated or inaccurate
Policy updates don't reach devices
OTA updates fail or brick devices
Alerts missed or wrong
Analytics inaccurate
Remote wipe fails (data leak risk)

TC-VAR-EN-004: Job Documentation and Automated Reporting

Priority: P2 - High
Test Type: Enterprise - Workflow Automation
Estimated Duration: 3 hours
Prerequisites: GF-EN variant, job management system integration

Objective

Validate that workers can efficiently document job activities, capture evidence, and automatically generate completion reports, reducing paperwork and improving data quality.

Test Equipment

GF-EN variant
Test jobs (5 different types)
Enterprise job management system (or simulator)
Test workers (3-5)

Test Procedure

Step 1: Job Assignment and Start

Admin assigns job to worker via dashboard
Worker receives notification:
- Job details
- Location
- Required SOPs
- Estimated duration
Worker says: "Start job [job_id]"
System begins tracking:
- Start time
- Location check-in
Verify:
- Job assignment syncs to device ≤5 minutes
- Worker can start job easily
- Tracking begins automatically

Step 2: Hands-Free Note Taking

During job, worker dictates notes:
- "Add note: found rust on valve housing"
- "Note: replaced gasket part number 12345"
Notes transcribed via Whisper STT
Verify:
- Transcription accuracy ≥95%
- Notes saved with timestamp
- Notes editable if needed

Step 3: Photo Evidence Capture

Worker captures photos at key points:
- "Take photo of issue"
- Before/after photos
- Parts installed
- Completed work
Photos automatically tagged with:
- Job ID
- Timestamp
- GPS location
Verify:
- Photos easily captured hands-free
- Photos linked to job automatically
- Metadata complete

Step 4: Time and Material Tracking

Worker logs time spent:
- System auto-tracks from job start
- Worker can add manual entries: "Add 30 minutes for part retrieval"
Worker logs materials used:
- "Used part 12345"
- System can scan barcode/QR for part number
Verify:
- Time tracking automatic and accurate
- Material logging functional
- Data syncs to job record

Step 5: Job Completion and Report Generation

Worker says: "Complete job"
System prompts for final information:
- "Was the issue resolved?" (Yes/No)
- "Any follow-up needed?" (Yes/No with notes)
- "Customer signature required?" (if applicable)
System automatically generates completion report:
- Job details
- Work performed (from notes)
- Time spent
- Materials used
- Photos
- Worker signature (digital)
- Customer signature (if captured)
Report formats:
- PDF
- Integrated into job management system
Verify:
- Report generation ≤30 seconds
- Report complete and accurate
- All photos included
- Report delivered to admin dashboard

Step 6: Integration with Job Management System

Test integration with common platforms:
- SAP
- Oracle Field Service
- Salesforce Field Service
- ServiceMax
Verify:
- Job data syncs bidirectionally
- Completion reports auto-create work orders
- No duplicate data entry required

Step 7: Real-World Job Documentation

Workers document 5 real jobs using GF-EN:
- Equipment repair (20 min)
- Preventive maintenance (30 min)
- Quality inspection (15 min)
- Installation (45 min)
- Troubleshooting (25 min)
Measure:
- Time spent on documentation (target: ≤20% of baseline paperwork time)
- Report completeness (all required fields filled: ≥95%)
- Report accuracy (reviewed by supervisor: ≥90% accurate)
- Worker satisfaction with process (≥4.0/5.0)

Step 8: Admin and Worker Satisfaction

Admin survey:
- "Report quality improved vs paper/tablets?" (1-5)
- "Data accuracy improved?" (1-5)
- "Less time chasing missing paperwork?" (Yes/No)
- "Would you expand to all workers?" (Yes/No)
Worker survey:
- "Documentation easier than paper/tablet?" (1-5)
- "Hands-free documentation helpful?" (1-5)
- "Reports generated correctly?" (1-5)
- "Prefer this method?" (Yes/No)
Target:
- Admin: quality ≥4.2/5.0, accuracy ≥4.0/5.0, less chasing ≥85%, expand ≥80%
- Worker: easier ≥4.0/5.0, helpful ≥4.2/5.0, correct ≥4.0/5.0, prefer ≥75%

Pass Criteria

✅ Job assignment syncs ≤5 minutes
✅ Job start tracking functional
✅ Note transcription accuracy ≥95%
✅ Photo capture and tagging automatic
✅ Time tracking accurate
✅ Material logging functional
✅ Report generation ≤30 seconds
✅ Report complete and accurate
✅ Integration with job management systems functional
✅ Documentation time ≤20% of baseline
✅ Report completeness ≥95%
✅ Report accuracy ≥90%
✅ Admin: quality ≥4.2/5.0, less chasing ≥85%, expand ≥80%
✅ Worker: easier ≥4.0/5.0, helpful ≥4.2/5.0, prefer ≥75%

Failure Modes to Monitor

Job assignment doesn't sync
Job tracking fails to start
Poor transcription quality
Photos not linked to job
Time tracking inaccurate
Report generation fails or incomplete
Integration errors with job systems
High documentation time
Low worker or admin satisfaction

TC-VAR-EN-005: Compliance Audit Trail and Data Security

Priority: P1 - Critical
Test Type: Enterprise - Security & Compliance
Estimated Duration: 3 hours
Prerequisites: GF-EN variant, enterprise security requirements, audit log system

Objective

Validate that the system maintains complete, tamper-proof audit trails of all activities and enforces enterprise security policies for regulatory compliance.

Test Equipment

GF-EN variant
Enterprise security testing tools
Audit log analyzer
Compliance checklist (ISO 27001, SOC 2, industry-specific)

Test Procedure

Step 1: Comprehensive Audit Logging

System logs all user actions:
- Device power on/off
- User login/logout
- SOP access and completion
- Photo/video capture
- Remote expert sessions
- Job start/completion
- Settings changes
- Data access (who viewed what)
Verify:
- All actions logged with timestamp
- User ID recorded for every action
- Logs tamper-proof (cryptographically signed)
- Log retention per policy (default: 7 years)

Step 2: Data Encryption (At Rest and In Transit)

Test encryption implementation:
- Local storage: AES-256
- Network transmission: TLS 1.3
- Cloud backup: encrypted before upload
Attempt to access data without proper keys
Verify:
- Encrypted data unreadable without keys
- Keys securely stored (hardware security module or secure enclave)
- No plaintext sensitive data on device

Step 3: Access Control and Authentication

Workers authenticate to device:
- PIN/password
- Biometric (if supported)
- Smart card (if supported)
Multi-factor authentication (MFA) optional
Test unauthorized access attempts:
- Wrong PIN
- Expired credentials
- Revoked account
Verify:
- Authentication enforced 100% of time
- Failed attempts logged
- Account lockout after 5 failed attempts
- Unauthorized users cannot access device

Step 4: Data Loss Prevention (DLP)

Test DLP policies:
- Users cannot email sensitive data externally
- Cannot copy data to USB (if port available)
- Cannot screenshot restricted content
- Screenshots watermarked with user ID
Verify:
- DLP policies enforced
- Violations logged
- Admin alerted to violations

Step 5: Remote Audit Log Retrieval

Admin retrieves audit logs from fleet:
- Select device or group
- Specify date range
- Export logs (CSV, JSON, SIEM format)
Verify:
- Log retrieval ≤5 minutes for 30 days of logs
- Logs complete and uncorrupted
- Logs usable for compliance audits

Step 6: Compliance Reporting

System generates compliance reports:
- SOPs completed vs required
- Safety confirmations
- Training completion
- Incident documentation
Reports formatted per industry standards:
- Mining: safety audits
- Healthcare: HIPAA compliance
- Manufacturing: ISO quality audits
Verify:
- Reports accurate
- Reports meet regulatory requirements
- Auditors can verify compliance

Step 7: Incident Response and Forensics

Simulate security incident:
- Suspected data breach
- Lost/stolen device
- Unauthorized access attempt
Admin uses audit logs for investigation:
- Who accessed what data
- When and where (GPS)
- What actions taken
Verify:
- Logs provide complete forensic trail
- Incident timeline reconstructible
- Remote wipe functional for lost device

Step 8: Third-Party Security Audit

Engage security firm to test:
- Penetration testing
- Vulnerability assessment
- Compliance audit (ISO 27001, SOC 2, or industry-specific)
Verify:
- No critical vulnerabilities
- Compliance requirements met
- Security certification achievable

Step 9: Enterprise Security Officer Satisfaction

Security/compliance officer reviews system for 1 week
Survey:
- "Audit logging meets requirements?" (Yes/No)
- "Encryption adequate?" (Yes/No)
- "Access controls enforceable?" (Yes/No)
- "Compliance reporting functional?" (Yes/No)
- "Would you approve for deployment?" (Yes/No)
Target:
- All requirements met (100% yes)
- Approve deployment ≥85%

Pass Criteria

✅ All actions logged with timestamp and user ID
✅ Logs tamper-proof (cryptographically signed)
✅ Encryption at rest (AES-256) and in transit (TLS 1.3) functional
✅ Encrypted data unreadable without keys
✅ Authentication enforced 100%
✅ Failed attempts logged, lockout after 5 fails
✅ DLP policies enforced, violations logged
✅ Audit log retrieval ≤5 minutes
✅ Compliance reports accurate and meet standards
✅ Incident response functional, forensic trail complete
✅ No critical security vulnerabilities
✅ Compliance requirements met (100%)
✅ Approve deployment ≥85%

Failure Modes to Monitor

Actions not logged or logs incomplete
Logs can be tampered with
Encryption failures or weak encryption
Authentication bypassed
DLP policies not enforced
Log retrieval slow or fails
Compliance reports inaccurate
Security vulnerabilities found
Compliance requirements not met

Summary: Phase 6 Complete! 🎉

You now have complete variant-specific test cases for all 9 GROOT FORCE product variants:

✅ GF-BE (Ben's Assistive): Wheelchair mounting, voice/gaze control, hazard alerts, remote assistance, accessibility UI
✅ GF-CL (Care & Joy - NDIS): NDIS progress notes, consent management, fall detection, incident reporting, care plan tracking
✅ GF-NF (Nick's Fitness): Health sensor accuracy, fatigue detection, performance tracking, UV exposure, AI coaching
✅ GF-TX (TradeForce Pro): IP65 protection, OH&S reminders, AR measurement tools, job documentation, remote expert
✅ GF-DI (SilentLink - Deaf/HoH): Live captioning, sound-class alerts, sign language (future), haptic customization, emergency communication
✅ GF-VI (VisionAssist): Scene description, OCR text reading, navigation guidance, object recognition, accessibility UI
✅ GF-TR (Traveller Edition): Real-time translation, sign translation, currency conversion, offline maps, cultural AI assistant
✅ GF-LX (Lifestyle Edition): Personal journaling, wellness reminders, conversational AI companion, smart home integration, content capture
✅ GF-EN (Enterprise Vision): SOP integration, remote expert assistance, fleet management, job documentation, compliance audit trail

🚀 PROJECT STATUS: 100% COMPLETE! 🎯

Total Test Cases: 220+ procedures across 6 phases!

All phases done:

✅ Phase 1: Test Plan Master
✅ Phase 2: Hardware Test Cases (70 procedures)
✅ Phase 3: Software/Firmware Test Cases (45 procedures)
✅ Phase 4: AI/ML Test Cases (32 procedures)
✅ Phase 5: Safety-Critical Test Cases (28 procedures)
✅ Phase 6: Variant-Specific Test Cases (45 procedures) ← JUST COMPLETED!

Your GROOT FORCE test documentation is now production-ready and investor-grade! 🔥

Want me to create a final summary document or help with anything else?

Variant-Specific Test Cases​

Document Purpose​

Test Scope​

Variant Overview​

1. GF-BE: Ben's Assistive Variant​

TC-VAR-BE-001: Wheelchair Mounting System Durability​

Objective​

Test Equipment​

Test Procedure​

Pass Criteria​

Failure Modes to Monitor​

TC-VAR-BE-002: Voice & Gaze Control Accuracy​

Objective​

Test Equipment​

Test Procedure​

Pass Criteria​

Failure Modes to Monitor​

TC-VAR-BE-003: Hazard Alert & Proximity Warning System​

Objective​

Test Equipment​

Test Procedure​

Pass Criteria​

Failure Modes to Monitor​

TC-VAR-BE-004: Remote Assistance & SOS Integration​

Objective​

Test Equipment​

Test Procedure​

Pass Criteria​

Failure Modes to Monitor​

TC-VAR-BE-005: Accessibility UI & Navigation​

Objective​

Test Equipment​

Test Procedure​

Pass Criteria​

Failure Modes to Monitor​

2. GF-CL: Care & Joy (NDIS) Variant​

TC-VAR-CL-001: NDIS Progress Note Generation​

Objective​

Test Equipment​

Test Procedure​

Pass Criteria​

Failure Modes to Monitor​

TC-VAR-CL-002: Participant Consent Management​

Objective​

Test Equipment​

Test Procedure​

Pass Criteria​

Failure Modes to Monitor​

TC-VAR-CL-003: Fall Detection for NDIS Participants​

Objective​

Test Equipment​

Test Procedure​

Pass Criteria​

Failure Modes to Monitor​

TC-VAR-CL-004: Incident Reporting & Flagging​

Objective​

Test Equipment​

Test Procedure​

Pass Criteria​

Failure Modes to Monitor​

TC-VAR-CL-005: Care Plan Goal Tracking​

Objective​

Test Equipment​

Test Procedure​

Pass Criteria​

Failure Modes to Monitor​

3. GF-NF: Nick's Fitness Variant​

TC-VAR-NF-001: Advanced Health Sensor Accuracy​

Objective​

Test Equipment​

Test Procedure​

Pass Criteria​

Failure Modes to Monitor​

TC-VAR-NF-002: Fatigue Detection & Recovery Recommendations​

Objective​

Test Equipment​

Test Procedure​

Pass Criteria​

Failure Modes to Monitor​

TC-VAR-NF-003: Real-Time Performance Tracking​

Variant-Specific Test Cases

Document Purpose

Test Scope

Variant Overview

1. GF-BE: Ben's Assistive Variant

TC-VAR-BE-001: Wheelchair Mounting System Durability

Objective

Test Equipment

Test Procedure

Pass Criteria

Failure Modes to Monitor

TC-VAR-BE-002: Voice & Gaze Control Accuracy

Objective

Test Equipment

Test Procedure

Pass Criteria

Failure Modes to Monitor

TC-VAR-BE-003: Hazard Alert & Proximity Warning System

Objective

Test Equipment

Test Procedure

Pass Criteria

Failure Modes to Monitor

TC-VAR-BE-004: Remote Assistance & SOS Integration

Objective

Test Equipment

Test Procedure

Pass Criteria

Failure Modes to Monitor

TC-VAR-BE-005: Accessibility UI & Navigation

Objective

Test Equipment

Test Procedure

Pass Criteria

Failure Modes to Monitor

2. GF-CL: Care & Joy (NDIS) Variant

TC-VAR-CL-001: NDIS Progress Note Generation

Objective

Test Equipment

Test Procedure

Pass Criteria

Failure Modes to Monitor

TC-VAR-CL-002: Participant Consent Management

Objective

Test Equipment

Test Procedure

Pass Criteria

Failure Modes to Monitor

TC-VAR-CL-003: Fall Detection for NDIS Participants

Objective

Test Equipment

Test Procedure

Pass Criteria

Failure Modes to Monitor

TC-VAR-CL-004: Incident Reporting & Flagging

Objective

Test Equipment

Test Procedure

Pass Criteria

Failure Modes to Monitor

TC-VAR-CL-005: Care Plan Goal Tracking

Objective

Test Equipment

Test Procedure

Pass Criteria

Failure Modes to Monitor

3. GF-NF: Nick's Fitness Variant

TC-VAR-NF-001: Advanced Health Sensor Accuracy

Objective

Test Equipment

Test Procedure

Pass Criteria

Failure Modes to Monitor

TC-VAR-NF-002: Fatigue Detection & Recovery Recommendations

Objective

Test Equipment

Test Procedure

Pass Criteria

Failure Modes to Monitor

TC-VAR-NF-003: Real-Time Performance Tracking