wifi-densepose/plans/phase1-specification/functional-spec.md

Functional Specification

WiFi-DensePose System

Document Information

• Version: 1.0
• Date: 2025-01-07
• Project: InvisPose - WiFi-Based Dense Human Pose Estimation
• Status: Draft

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1. Introduction

1.1 Purpose

This document defines the functional requirements and behaviors of the WiFi-DensePose system, specifying what the system must do to meet user needs across healthcare, retail, and security domains.

1.2 Scope

The functional specification covers all user-facing features, system behaviors, data processing workflows, and integration capabilities required for the WiFi-based human pose estimation platform.

1.3 Functional Overview

The system transforms WiFi Channel State Information (CSI) into real-time human pose estimates through neural network processing, providing privacy-preserving human sensing capabilities with 87.2% accuracy.

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2. Core Functional Requirements

2.1 CSI Data Collection and Processing

2.1.1 WiFi Signal Acquisition

Function: Extract Channel State Information from compatible WiFi routers

• Input: Raw WiFi signals from 3×3 MIMO antenna arrays
• Processing: Real-time CSI extraction with amplitude and phase data
• Output: Structured CSI data streams with temporal coherence
• Frequency: Continuous operation at 10-30 Hz sampling rate

Acceptance Criteria:

• Successfully extract CSI from Atheros-based routers
• Maintain data integrity across extended operation periods
• Handle network interruptions with automatic reconnection
• Support multiple router types with unified data format

2.1.2 Signal Preprocessing

Function: Clean and normalize raw CSI data for neural network input

• Phase Unwrapping: Correct phase discontinuities and wrapping artifacts
• Temporal Filtering: Apply moving average and linear detrending
• Background Subtraction: Remove static environmental components
• Noise Reduction: Filter systematic noise and interference

Processing Pipeline:

Raw CSI → Phase Unwrapping → Temporal Filtering → 
Background Subtraction → Noise Reduction → Normalized CSI

Acceptance Criteria:

• Achieve signal-to-noise ratio improvement of 10dB minimum
• Maintain temporal coherence across processing stages
• Adapt to environmental changes automatically
• Process data streams without introducing latency >10ms

2.1.3 Environmental Calibration

Function: Establish baseline measurements for background subtraction

• Baseline Capture: Record empty environment CSI patterns
• Adaptive Calibration: Update baselines for environmental changes
• Multi-Environment: Support different room configurations
• Drift Compensation: Correct for systematic signal drift

Calibration Process:

1. Capture 60-second baseline with no human presence
2. Establish statistical models for background variation
3. Monitor for environmental changes requiring recalibration
4. Update baselines automatically or on user request

2.2 Neural Network Inference

2.2.1 Modality Translation Network

Function: Convert 1D CSI signals to 2D spatial representations

• Dual-Branch Processing: Separate amplitude and phase encoders
• Feature Fusion: Combine modality-specific features
• Spatial Upsampling: Generate 720×1280 spatial representations
• Temporal Consistency: Maintain coherence across frames

Network Architecture:

CSI Input (3×3×N) → Amplitude Branch → Feature Fusion → 
Phase Branch → Upsampling → Spatial Features (720×1280×3)

Performance Requirements:

• Processing latency <50ms on GPU hardware
• Maintain temporal consistency across frame sequences
• Support batch processing for efficiency
• Graceful degradation on CPU-only systems

2.2.2 DensePose Estimation

Function: Extract dense human pose from spatial features

• Body Part Detection: Identify 24 anatomical regions
• UV Coordinate Mapping: Generate dense correspondence maps
• Keypoint Extraction: Detect 17 major body keypoints
• Confidence Scoring: Provide detection confidence metrics

Output Format:

• Dense pose masks for 24 body parts
• UV coordinates for surface mapping
• 2D keypoint coordinates with confidence scores
• Bounding boxes for detected persons

2.2.3 Multi-Person Tracking

Function: Track multiple individuals across frame sequences

• Person Detection: Identify up to 5 individuals simultaneously
• ID Assignment: Maintain consistent person identifiers
• Occlusion Handling: Track through temporary occlusions
• Trajectory Smoothing: Apply temporal filtering for stability

Tracking Features:

• Kalman filtering for position prediction
• Hungarian algorithm for ID assignment
• Confidence-based track management
• Automatic track initialization and termination

2.3 Real-Time Processing Pipeline

2.3.1 Data Flow Management

Function: Orchestrate end-to-end processing pipeline

• Buffer Management: Handle continuous data streams
• Queue Processing: Manage processing queues efficiently
• Resource Allocation: Optimize CPU/GPU utilization
• Error Recovery: Handle processing failures gracefully

Pipeline Stages:

1. CSI Data Ingestion
2. Preprocessing and Normalization
3. Neural Network Inference
4. Post-processing and Tracking
5. Output Generation and Distribution

2.3.2 Performance Optimization

Function: Maintain real-time performance under varying loads

• Adaptive Processing: Scale processing based on available resources
• Frame Dropping: Skip frames under high load conditions
• Batch Optimization: Group operations for efficiency
• Memory Management: Prevent memory leaks and optimize usage

Optimization Strategies:

• Dynamic batch size adjustment
• GPU memory pooling
• Asynchronous processing pipelines
• Intelligent frame scheduling

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3. User Stories and Use Cases

3.1 Healthcare Domain User Stories

3.1.1 Elderly Care Monitoring

As a healthcare provider I want to monitor elderly patients for fall events and activity patterns So that I can provide immediate assistance and track health trends

Acceptance Criteria:

• System detects falls with 95% accuracy within 2 seconds
• Activity patterns are tracked and reported daily
• Alerts are sent immediately upon fall detection
• Privacy is maintained with no video recording

User Journey:

1. Caregiver configures fall detection sensitivity
2. System continuously monitors patient movement
3. Fall event triggers immediate alert to caregiver
4. System provides activity summary for health assessment

// TEST: Verify fall detection accuracy meets 95% threshold // TEST: Confirm activity tracking provides meaningful health insights // TEST: Validate alert delivery within 2-second requirement

3.1.2 Rehabilitation Progress Tracking

As a physical therapist I want to track patient movement and exercise compliance So that I can adjust treatment plans based on objective data

Acceptance Criteria:

• Exercise movements are accurately classified
• Progress metrics are calculated and visualized
• Compliance rates are tracked over time
• Integration with electronic health records

User Journey:

1. Therapist sets up exercise monitoring protocol
2. Patient performs prescribed exercises
3. System tracks movement quality and completion
4. Progress reports are generated for treatment planning

// TEST: Verify exercise classification accuracy for rehabilitation movements // TEST: Confirm progress metrics calculation and visualization // TEST: Validate EHR integration functionality

3.2 Retail Domain User Stories

3.2.1 Store Layout Optimization

As a retail manager I want to understand customer traffic patterns and zone popularity So that I can optimize store layout and product placement

Acceptance Criteria:

• Customer paths are tracked anonymously
• Zone dwell times are measured accurately
• Heatmaps show traffic density patterns
• A/B testing capabilities for layout changes

User Journey:

1. Manager configures store zones and tracking areas
2. System monitors customer movement throughout day
3. Analytics dashboard shows traffic patterns and insights
4. Manager uses data to optimize store layout

// TEST: Verify anonymous customer tracking maintains privacy // TEST: Confirm zone analytics provide actionable insights // TEST: Validate A/B testing framework for layout optimization

3.2.2 Queue Management

As a store operations manager I want to monitor checkout queue lengths and wait times So that I can optimize staffing and reduce customer wait times

Acceptance Criteria:

• Queue lengths are detected in real-time
• Wait times are calculated automatically
• Staff alerts when queues exceed thresholds
• Historical data for staffing optimization

User Journey:

1. Manager sets queue length and wait time thresholds
2. System monitors checkout areas continuously
3. Alerts are sent when thresholds are exceeded
4. Historical data guides staffing decisions

// TEST: Verify queue detection accuracy in various store layouts // TEST: Confirm wait time calculations are precise // TEST: Validate alert system for queue management

3.3 Security Domain User Stories

3.3.1 Perimeter Security Monitoring

As a security officer I want to monitor restricted areas for unauthorized access So that I can respond quickly to security breaches

Acceptance Criteria:

• Intrusion detection works through walls and obstacles
• Real-time alerts with location information
• Integration with existing security systems
• Audit trail for all security events

User Journey:

1. Security officer configures restricted zones
2. System monitors areas 24/7 without line-of-sight
3. Intrusion triggers immediate alert with location
4. Officer responds based on alert information

// TEST: Verify through-wall detection capability // TEST: Confirm real-time alert delivery with accurate location // TEST: Validate integration with security management systems

3.3.2 Building Occupancy Monitoring

As a facility manager I want to track building occupancy for safety and compliance So that I can ensure emergency evacuation procedures and capacity limits

Acceptance Criteria:

• Accurate person counting in all monitored areas
• Real-time occupancy dashboard
• Emergency evacuation support
• Compliance reporting for safety regulations

User Journey:

1. Manager configures occupancy limits for each area
2. System tracks person count continuously
3. Dashboard shows real-time occupancy status
4. Emergency mode provides evacuation support

// TEST: Verify person counting accuracy across different environments // TEST: Confirm occupancy dashboard provides real-time updates // TEST: Validate emergency evacuation support functionality

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4. Real-Time Streaming Requirements

4.1 Performance Requirements

4.1.1 Latency Requirements

End-to-End Latency: <100ms from CSI data to pose output

• CSI Processing: <20ms
• Neural Network Inference: <50ms
• Post-processing and Tracking: <20ms
• API Response Generation: <10ms

Streaming Latency: <50ms for WebSocket delivery

• Internal Processing: <30ms
• Network Transmission: <20ms

// TEST: Verify end-to-end latency meets <100ms requirement // TEST: Confirm WebSocket streaming latency <50ms // TEST: Validate latency consistency under varying loads

4.1.2 Throughput Requirements

Processing Throughput: 10-30 FPS depending on hardware

• Minimum: 10 FPS on CPU-only systems
• Optimal: 20 FPS on GPU-accelerated systems
• Maximum: 30 FPS on high-end hardware

Concurrent Streaming: Support 100+ simultaneous clients

• WebSocket connections: 100 concurrent
• REST API clients: 1000 concurrent
• Streaming bandwidth: 10 Mbps per client

// TEST: Verify processing throughput meets FPS requirements // TEST: Confirm system supports 100+ concurrent streaming clients // TEST: Validate bandwidth utilization stays within limits

4.2 Data Streaming Architecture

4.2.1 Multi-Protocol Support

WebSocket Streaming: Primary real-time protocol

• Binary and JSON message formats
• Compression for bandwidth optimization
• Automatic reconnection handling
• Client-side buffering for smooth playback

Server-Sent Events (SSE): Alternative streaming protocol

• HTTP-based streaming for firewall compatibility
• Automatic retry and reconnection
• Event-based message delivery
• Browser-native support

MQTT Streaming: IoT ecosystem integration

• QoS levels for reliability guarantees
• Topic-based message routing
• Retained messages for state persistence
• Scalable pub/sub architecture

// TEST: Verify WebSocket streaming handles reconnections gracefully // TEST: Confirm SSE provides reliable alternative streaming // TEST: Validate MQTT integration with IoT ecosystems

4.2.2 Adaptive Streaming

Quality Adaptation: Automatic quality adjustment based on network conditions

• Bandwidth detection and monitoring
• Dynamic frame rate adjustment
• Compression level optimization
• Graceful degradation strategies

Client Capability Detection: Optimize streaming for client capabilities

• Device performance assessment
• Network bandwidth measurement
• Display resolution adaptation
• Battery optimization for mobile clients

// TEST: Verify adaptive streaming adjusts to network conditions // TEST: Confirm client capability detection works accurately // TEST: Validate quality adaptation maintains user experience

4.3 Restream Integration Specifications

4.3.1 Platform Support

Supported Platforms: Multi-platform simultaneous streaming

• YouTube Live: RTMP streaming with custom overlays
• Twitch: Real-time pose visualization streams
• Facebook Live: Social media integration
• Custom RTMP: Enterprise and private platforms

Stream Configuration: Flexible streaming parameters

• Resolution: 720p, 1080p, 4K support
• Frame Rate: 15, 30, 60 FPS options
• Bitrate: Adaptive 1-10 Mbps
• Codec: H.264, H.265 support

// TEST: Verify simultaneous streaming to multiple platforms // TEST: Confirm stream quality meets platform requirements // TEST: Validate custom RTMP endpoint functionality

4.3.2 Visualization Pipeline

Pose Overlay Generation: Real-time visualization creation

• Skeleton rendering with customizable styles
• Confidence indicators and person IDs
• Background options (transparent, solid, custom)
• Multi-person color coding

Stream Composition: Video stream assembly

• Pose overlay compositing
• Background image/video integration
• Text overlay for metadata
• Logo and branding integration

Performance Optimization: Efficient video processing

• GPU-accelerated rendering
• Parallel processing pipelines
• Memory-efficient operations
• Real-time encoding optimization

// TEST: Verify pose overlay generation meets quality standards // TEST: Confirm stream composition handles multiple elements // TEST: Validate performance optimization maintains real-time processing

4.3.3 Stream Management

Connection Management: Robust streaming infrastructure

• Automatic reconnection on failures
• Stream health monitoring
• Bandwidth adaptation
• Error recovery procedures

Analytics and Monitoring: Stream performance tracking

• Viewer count monitoring
• Stream quality metrics
• Bandwidth utilization tracking
• Error rate monitoring

Configuration Management: Dynamic stream control

• Real-time parameter adjustment
• Stream start/stop control
• Platform-specific optimizations
• Scheduled streaming support

// TEST: Verify stream management handles connection failures // TEST: Confirm analytics provide meaningful insights // TEST: Validate configuration changes apply without interruption

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5. Domain-Specific Functional Requirements

3.1 Healthcare Monitoring

3.1.1 Fall Detection

Function: Detect and alert on fall events for elderly care

• Pattern Recognition: Identify rapid position changes
• Threshold Configuration: Adjustable sensitivity settings
• Alert Generation: Immediate notification on fall detection
• False Positive Reduction: Filter normal activities

Detection Algorithm:

Pose Trajectory Analysis → Velocity Calculation → 
Position Change Detection → Confidence Assessment → Alert Decision

Alert Criteria:

• Vertical position change >1.5m in <2 seconds
• Horizontal impact detection
• Sustained ground-level position >10 seconds
• Configurable sensitivity thresholds

3.1.2 Activity Monitoring

Function: Track patient mobility and activity patterns

• Activity Classification: Identify sitting, standing, walking, lying
• Mobility Metrics: Calculate movement frequency and duration
• Inactivity Detection: Alert on prolonged inactivity periods
• Daily Reports: Generate activity summaries

Monitored Activities:

• Walking patterns and gait analysis
• Sitting/standing transitions
• Sleep position monitoring
• Exercise and rehabilitation activities

3.1.3 Privacy-Preserving Analytics

Function: Generate health insights while protecting patient privacy

• Anonymous Data: No personally identifiable information
• Aggregated Metrics: Statistical summaries only
• Secure Storage: Encrypted local data storage
• Audit Trails: Comprehensive access logging

3.2 Retail Analytics

3.2.1 Customer Traffic Analysis

Function: Monitor customer movement and behavior patterns

• Traffic Counting: Real-time customer count tracking
• Zone Analytics: Movement between store zones
• Dwell Time: Time spent in specific areas
• Path Analysis: Customer journey mapping

Analytics Outputs:

• Hourly/daily traffic reports
• Zone popularity heatmaps
• Average dwell time by area
• Peak traffic period identification

3.2.2 Occupancy Management

Function: Monitor store capacity and density

• Real-Time Counts: Current occupancy levels
• Capacity Alerts: Notifications at threshold levels
• Queue Detection: Identify waiting areas and lines
• Social Distancing: Monitor spacing compliance

Capacity Features:

• Configurable occupancy limits
• Real-time dashboard displays
• Automated alert systems
• Historical occupancy trends

3.2.3 Layout Optimization

Function: Provide insights for store layout improvements

• Traffic Flow: Identify bottlenecks and dead zones
• Product Interaction: Monitor engagement with displays
• Conversion Analysis: Path-to-purchase tracking
• A/B Testing: Compare layout configurations

3.3 Security Applications

3.3.1 Intrusion Detection

Function: Monitor restricted areas for unauthorized access

• Perimeter Monitoring: Detect boundary crossings
• Through-Wall Detection: Monitor without line-of-sight
• Behavioral Analysis: Identify suspicious movement patterns
• Real-Time Alerts: Immediate security notifications

Detection Capabilities:

• Motion detection in restricted zones
• Loitering detection with configurable timeouts
• Multiple person alerts
• Integration with security systems

3.3.2 Access Control Integration

Function: Enhance physical security systems

• Zone-Based Monitoring: Different security levels by area
• Time-Based Rules: Schedule-dependent monitoring
• Credential Correlation: Link with access card systems
• Audit Logging: Comprehensive security event logs

3.3.3 Emergency Response

Function: Support emergency evacuation and response

• Occupancy Tracking: Real-time person counts by zone
• Evacuation Monitoring: Track movement during emergencies
• First Responder Support: Provide occupancy information
• Emergency Alerts: Automated emergency notifications

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4. API and Integration Functions

4.1 REST API Endpoints

4.1.1 Pose Data Access

Endpoints:

• GET /pose/latest - Current pose data
• GET /pose/history - Historical pose data
• GET /pose/stream - Real-time pose stream
• POST /pose/query - Custom pose queries

Response Format:

{
  "timestamp": "2025-01-07T04:46:32Z",
  "persons": [
    {
      "id": 1,
      "confidence": 0.87,
      "keypoints": [...],
      "dense_pose": {...},
      "bounding_box": {...}
    }
  ],
  "metadata": {
    "processing_time": 45,
    "frame_id": 12345
  }
}

4.1.2 System Control

Endpoints:

• POST /system/start - Start pose estimation
• POST /system/stop - Stop pose estimation
• GET /system/status - System health status
• POST /system/calibrate - Trigger calibration

4.1.3 Configuration Management

Endpoints:

• GET /config - Current configuration
• PUT /config - Update configuration
• GET /config/templates - Available templates
• POST /config/validate - Validate configuration

4.2 WebSocket Streaming

4.2.1 Real-Time Data Streams

Function: Provide low-latency pose data streaming

• Connection Management: Handle multiple concurrent clients
• Message Broadcasting: Efficient data distribution
• Automatic Reconnection: Client reconnection handling
• Rate Limiting: Prevent client overload

Stream Types:

• Pose data streams
• System status updates
• Alert notifications
• Performance metrics

4.2.2 Client Management

Function: Manage WebSocket client lifecycle

• Authentication: Secure client connections
• Subscription Management: Topic-based subscriptions
• Connection Monitoring: Health check and cleanup
• Error Handling: Graceful error recovery

4.3 External Integration

4.3.1 MQTT Publishing

Function: Integrate with IoT ecosystems

• Topic Structure: Hierarchical topic organization
• Message Formats: JSON and binary message support
• QoS Levels: Configurable quality of service
• Retained Messages: State persistence

MQTT Topics:

• wifi-densepose/pose/person/{id} - Individual pose data
• wifi-densepose/alerts/{type} - Alert notifications
• wifi-densepose/status - System status
• wifi-densepose/analytics/{domain} - Domain analytics

4.3.2 Webhook Integration

Function: Send real-time notifications to external services

• Event Triggers: Configurable event conditions
• Retry Logic: Automatic retry on failures
• Authentication: Support for various auth methods
• Payload Customization: Flexible message formats

Webhook Events:

• Person detection/departure
• Fall detection alerts
• System status changes
• Threshold violations

4.3.3 Restream Integration

Function: Live streaming to multiple platforms

• Multi-Platform: Simultaneous streaming to multiple services
• Video Encoding: Real-time video generation
• Stream Management: Automatic reconnection and quality adaptation
• Overlay Generation: Pose visualization overlays

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5. User Interface Functions

5.1 Web Dashboard

5.1.1 Real-Time Visualization

Function: Display live pose estimation results

• Pose Rendering: Real-time skeleton visualization
• Multi-Person Display: Color-coded person tracking
• Confidence Indicators: Visual confidence representation
• Background Options: Configurable visualization backgrounds

Visualization Features:

• Stick figure pose representation
• Dense pose heat maps
• Keypoint confidence visualization
• Trajectory tracking displays

5.1.2 System Monitoring

Function: Monitor system health and performance

• Performance Metrics: Real-time performance indicators
• Resource Usage: CPU, GPU, memory utilization
• Network Status: CSI data stream health
• Error Reporting: System error and warning displays

5.1.3 Configuration Interface

Function: System configuration and control

• Parameter Adjustment: Real-time parameter tuning
• Template Selection: Domain-specific configuration templates
• Calibration Control: Manual calibration triggers
• Alert Configuration: Threshold and notification settings

5.2 Mobile Interface

5.2.1 Responsive Design

Function: Mobile-optimized interface for monitoring

• Touch Interface: Mobile-friendly controls
• Responsive Layout: Adaptive screen sizing
• Offline Capability: Basic functionality without connectivity
• Push Notifications: Mobile alert delivery

5.2.2 Quick Actions

Function: Essential controls for mobile users

• System Start/Stop: Basic system control
• Alert Acknowledgment: Quick alert responses
• Status Overview: System health summary
• Emergency Controls: Rapid emergency response

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6. Data Management Functions

6.1 Data Storage

6.1.1 Pose Data Storage

Function: Store pose estimation results for analysis

• Time-Series Storage: Efficient temporal data storage
• Compression: Data compression for storage efficiency
• Indexing: Fast query performance
• Retention Policies: Configurable data retention

Storage Schema:

pose_data:
  - timestamp (primary key)
  - person_id
  - pose_keypoints
  - confidence_scores
  - metadata

6.1.2 Configuration Storage

Function: Persist system configuration and settings

• Version Control: Configuration change tracking
• Backup/Restore: Configuration backup capabilities
• Template Management: Pre-configured templates
• Validation: Configuration integrity checking

6.1.3 Analytics Storage

Function: Store aggregated analytics and reports

• Domain-Specific: Separate storage for different domains
• Aggregation: Pre-computed analytics for performance
• Export Capabilities: Data export in multiple formats
• Privacy Compliance: Anonymized data storage

6.2 Data Processing

6.2.1 Batch Analytics

Function: Process historical data for insights

• Trend Analysis: Long-term pattern identification
• Statistical Analysis: Comprehensive statistical metrics
• Report Generation: Automated report creation
• Data Mining: Advanced pattern discovery

6.2.2 Real-Time Analytics

Function: Generate live insights from streaming data

• Stream Processing: Real-time data aggregation
• Threshold Monitoring: Live threshold violation detection
• Anomaly Detection: Real-time anomaly identification
• Alert Generation: Immediate alert processing

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7. Quality Assurance Functions

7.1 Testing and Validation

7.1.1 Automated Testing

Function: Comprehensive automated test coverage

• Unit Testing: Component-level test coverage
• Integration Testing: End-to-end pipeline testing
• Performance Testing: Load and stress testing
• Regression Testing: Continuous validation

7.1.2 Hardware Simulation

Function: Test without physical hardware

• CSI Simulation: Synthetic CSI data generation
• Scenario Testing: Predefined test scenarios
• Environment Simulation: Various deployment conditions
• Validation Testing: Algorithm validation

7.2 Monitoring and Diagnostics

7.2.1 System Health Monitoring

Function: Continuous system health assessment

• Performance Monitoring: Real-time performance tracking
• Resource Monitoring: Hardware resource utilization
• Error Detection: Automatic error identification
• Predictive Maintenance: Proactive issue identification

7.2.2 Diagnostic Tools

Function: Troubleshooting and problem resolution

• Log Analysis: Comprehensive log analysis tools
• Performance Profiling: Detailed performance analysis
• Network Diagnostics: CSI data stream analysis
• Debug Interfaces: Developer debugging tools

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8. Acceptance Criteria

8.1 Functional Acceptance

• Pose Detection: Successfully detect human poses with 87.2% AP@50
• Multi-Person: Track up to 5 individuals simultaneously
• Real-Time: Maintain <100ms end-to-end latency
• Domain Functions: All domain-specific features operational

8.2 Integration Acceptance

• API Endpoints: All specified endpoints functional
• WebSocket Streaming: Real-time data streaming operational
• External Integration: MQTT, webhooks, and Restream functional
• Dashboard: Web interface fully operational

8.3 Performance Acceptance

• Throughput: Achieve 10-30 FPS processing rates
• Reliability: 99.5% uptime over testing period
• Scalability: Support 100+ concurrent API clients
• Resource Usage: Operate within specified hardware limits

// TEST: Validate CSI data extraction from all supported router types // TEST: Verify neural network inference accuracy meets AP@50 targets // TEST: Confirm multi-person tracking maintains ID consistency // TEST: Validate real-time performance under various load conditions // TEST: Test all API endpoints for correct functionality // TEST: Verify WebSocket streaming handles multiple concurrent clients // TEST: Validate domain-specific functions for healthcare, retail, security // TEST: Confirm external integrations work with MQTT, webhooks, Restream // TEST: Test web dashboard functionality across different browsers // TEST: Validate data storage and retrieval operations // TEST: Verify system monitoring and diagnostic capabilities // TEST: Confirm automated testing framework covers all components