Functional Specification

#WiFi-DensePose System

#Document Information

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

---

#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.

---

#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

---

#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

---

#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

---

#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

---

#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:

1{
2  "timestamp": "2025-01-07T04:46:32Z",
3  "persons": [
4    {
5      "id": 1,
6      "confidence": 0.87,
7      "keypoints": [...],
8      "dense_pose": {...},
9      "bounding_box": {...}
10    }
11  ],
12  "metadata": {
13    "processing_time": 45,
14    "frame_id": 12345
15  }
16}

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

---

#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

---

#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

---

#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

---

#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