# Technical 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 provides detailed technical specifications for the WiFi-DensePose system implementation, including architecture design, component interfaces, data structures, and implementation strategies.

### 1.2 Scope
The technical specification covers system architecture, neural network design, data processing pipelines, API implementation, hardware interfaces, and deployment considerations.

### 1.3 Technical Overview
The system employs a modular architecture with five primary components: Hardware Interface Layer, Neural Network Pipeline, Pose Estimation Engine, API Services, and Configuration Management.

---

## 2. System Architecture

### 2.1 High-Level Architecture

```
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   WiFi Routers  │    │  CSI Receiver   │    │ Neural Network  │
│   (Hardware)    │───▶│    Module       │───▶│    Pipeline     │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                                        │
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Web Dashboard │◄───│  API Services   │◄───│ Pose Estimation │
│   (Frontend)    │    │    Module       │    │     Engine      │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │
                       ┌─────────────────┐
                       │ Configuration   │
                       │   Management    │
                       └─────────────────┘
```

### 2.2 Component Architecture

#### 2.2.1 Hardware Interface Layer
**Purpose**: Interface with WiFi hardware for CSI data extraction
**Components**:
- CSI Data Collector
- Router Communication Manager
- Signal Preprocessor
- Data Stream Manager

**Technology Stack**:
- Python 3.8+ with asyncio for concurrent processing
- Socket programming for UDP data streams
- NumPy for signal processing operations
- Threading for parallel data collection

#### 2.2.2 Neural Network Pipeline
**Purpose**: Transform CSI signals to pose estimates
**Components**:
- Modality Translation Network
- DensePose Estimation Network
- Feature Fusion Module
- Temporal Consistency Filter

**Technology Stack**:
- PyTorch 1.12+ for deep learning framework
- CUDA 11.6+ for GPU acceleration
- TorchVision for computer vision utilities
- OpenCV for image processing operations

#### 2.2.3 Pose Estimation Engine
**Purpose**: Orchestrate end-to-end processing pipeline
**Components**:
- Pipeline Coordinator
- Multi-Person Tracker
- Performance Monitor
- Error Recovery Manager

**Technology Stack**:
- Python asyncio for asynchronous processing
- Threading and multiprocessing for parallelization
- Queue management for data flow control
- Logging framework for monitoring

#### 2.2.4 API Services Module
**Purpose**: Provide external interfaces and streaming
**Components**:
- FastAPI REST Server
- WebSocket Manager
- Streaming Service
- Authentication Handler

**Technology Stack**:
- FastAPI 0.95+ for REST API framework
- WebSockets for real-time communication
- FFmpeg for video encoding
- Pydantic for data validation

#### 2.2.5 Configuration Management
**Purpose**: Handle system configuration and templates
**Components**:
- Configuration Parser
- Template Manager
- Validation Engine
- Runtime Configuration

**Technology Stack**:
- YAML for configuration files
- JSON Schema for validation
- File system monitoring for dynamic updates
- Environment variable integration

### 2.3 Data Flow Architecture

```
CSI Raw Data → Preprocessing → Neural Network → Post-processing → Output
     │              │              │               │             │
     ▼              ▼              ▼               ▼             ▼
  UDP Stream    Signal Clean   Feature Extract   Tracking    API/Stream
  Buffer Mgmt   Calibration    Pose Estimation   Smoothing   Distribution
  Error Handle  Noise Filter   Multi-Person      ID Assign   Visualization
```

---

## 3. Neural Network Design

### 3.1 Modality Translation Network

#### 3.1.1 Architecture Overview
**Input**: CSI tensor (3×3×N) where N is temporal window
**Output**: Spatial features (720×1280×3) compatible with DensePose

**Network Structure**:
```python
class ModalityTranslationNetwork(nn.Module):
    def __init__(self):
        # Amplitude branch encoder
        self.amplitude_encoder = nn.Sequential(
            nn.Conv1d(9, 64, kernel_size=3, padding=1),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            nn.Conv1d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.AdaptiveAvgPool1d(256)
        )
        
        # Phase branch encoder
        self.phase_encoder = nn.Sequential(
            nn.Conv1d(9, 64, kernel_size=3, padding=1),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            nn.Conv1d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.AdaptiveAvgPool1d(256)
        )
        
        # Feature fusion and upsampling
        self.fusion_network = nn.Sequential(
            nn.Linear(512, 1024),
            nn.ReLU(),
            nn.Linear(1024, 720*1280*3),
            nn.Sigmoid()
        )
```

#### 3.1.2 CSI Preprocessing Pipeline
**Phase Unwrapping Algorithm**:
```python
def unwrap_phase(phase_data):
    """
    Unwrap CSI phase data to remove 2π discontinuities
    """
    unwrapped = np.unwrap(phase_data, axis=-1)
    # Apply linear detrending
    detrended = signal.detrend(unwrapped, axis=-1)
    # Temporal filtering
    filtered = apply_moving_average(detrended, window=5)
    return filtered

def apply_moving_average(data, window=5):
    """
    Apply moving average filter for noise reduction
    """
    kernel = np.ones(window) / window
    return np.convolve(data, kernel, mode='same')
```

**Amplitude Processing**:
```python
def process_amplitude(amplitude_data):
    """
    Process CSI amplitude data for neural network input
    """
    # Convert to dB scale
    amplitude_db = 20 * np.log10(np.abs(amplitude_data) + 1e-10)
    # Normalize to [0, 1] range
    normalized = (amplitude_db - amplitude_db.min()) / (amplitude_db.max() - amplitude_db.min())
    return normalized
```

#### 3.1.3 Feature Fusion Strategy
**Fusion Architecture**:
- Concatenate amplitude and phase features
- Apply fully connected layers for dimension reduction
- Use residual connections for gradient flow
- Apply dropout for regularization

### 3.2 DensePose Integration

#### 3.2.1 Network Adaptation
**Base Architecture**: DensePose-RCNN with ResNet-FPN backbone
**Modifications**:
- Replace RGB input with WiFi-translated features
- Adapt feature pyramid network for WiFi domain
- Modify region proposal network for WiFi characteristics
- Fine-tune detection heads for WiFi-specific patterns

#### 3.2.2 Transfer Learning Framework
**Teacher-Student Architecture**:
```python
class TransferLearningFramework:
    def __init__(self):
        self.teacher_model = load_pretrained_densepose()
        self.student_model = WiFiDensePoseModel()
        self.translation_network = ModalityTranslationNetwork()
    
    def knowledge_distillation_loss(self, wifi_features, image_features):
        """
        Compute knowledge distillation loss between teacher and student
        """
        teacher_output = self.teacher_model(image_features)
        student_output = self.student_model(wifi_features)
        
        # Feature matching loss
        feature_loss = F.mse_loss(student_output.features, teacher_output.features)
        
        # Pose estimation loss
        pose_loss = F.cross_entropy(student_output.poses, teacher_output.poses)
        
        return feature_loss + pose_loss
```

### 3.3 Multi-Person Tracking

#### 3.3.1 Tracking Algorithm
**Hungarian Algorithm Implementation**:
```python
class MultiPersonTracker:
    def __init__(self, max_persons=5):
        self.max_persons = max_persons
        self.active_tracks = {}
        self.next_id = 1
        
    def update(self, detections):
        """
        Update tracks with new detections using Hungarian algorithm
        """
        if not self.active_tracks:
            # Initialize tracks for first frame
            return self.initialize_tracks(detections)
        
        # Compute cost matrix
        cost_matrix = self.compute_cost_matrix(detections)
        
        # Solve assignment problem
        assignments = self.hungarian_assignment(cost_matrix)
        
        # Update tracks
        return self.update_tracks(detections, assignments)
    
    def compute_cost_matrix(self, detections):
        """
        Compute cost matrix for track-detection assignment
        """
        costs = np.zeros((len(self.active_tracks), len(detections)))
        
        for i, track in enumerate(self.active_tracks.values()):
            for j, detection in enumerate(detections):
                # Compute distance-based cost
                distance = np.linalg.norm(track.position - detection.position)
                # Add appearance similarity cost
                appearance_cost = 1 - self.compute_appearance_similarity(track, detection)
                costs[i, j] = distance + appearance_cost
        
        return costs
```

#### 3.3.2 Kalman Filtering
**State Prediction Model**:
```python
class KalmanTracker:
    def __init__(self):
        # State vector: [x, y, vx, vy, ax, ay]
        self.state = np.zeros(6)
        
        # State transition matrix
        self.F = np.array([
            [1, 0, 1, 0, 0.5, 0],
            [0, 1, 0, 1, 0, 0.5],
            [0, 0, 1, 0, 1, 0],
            [0, 0, 0, 1, 0, 1],
            [0, 0, 0, 0, 1, 0],
            [0, 0, 0, 0, 0, 1]
        ])
        
        # Measurement matrix
        self.H = np.array([
            [1, 0, 0, 0, 0, 0],
            [0, 1, 0, 0, 0, 0]
        ])
        
        # Process and measurement noise
        self.Q = np.eye(6) * 0.1  # Process noise
        self.R = np.eye(2) * 1.0  # Measurement noise
        self.P = np.eye(6) * 100  # Initial covariance
    
    def predict(self):
        """Predict next state"""
        self.state = self.F @ self.state
        self.P = self.F @ self.P @ self.F.T + self.Q
        return self.state[:2]  # Return position
    
    def update(self, measurement):
        """Update state with measurement"""
        y = measurement - self.H @ self.state
        S = self.H @ self.P @ self.H.T + self.R
        K = self.P @ self.H.T @ np.linalg.inv(S)
        
        self.state = self.state + K @ y
        self.P = (np.eye(6) - K @ self.H) @ self.P
```

---

## 4. Hardware Interface Implementation

### 4.1 CSI Data Collection

#### 4.1.1 Router Communication Protocol
**UDP Socket Implementation**:
```python
class CSIReceiver:
    def __init__(self, port=5500, buffer_size=1024):
        self.port = port
        self.buffer_size = buffer_size
        self.socket = None
        self.running = False
        
    async def start_collection(self):
        """Start CSI data collection"""
        self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
        self.socket.bind(('0.0.0.0', self.port))
        self.socket.setblocking(False)
        self.running = True
        
        while self.running:
            try:
                data, addr = await asyncio.wait_for(
                    self.socket.recvfrom(self.buffer_size), 
                    timeout=1.0
                )
                await self.process_csi_packet(data, addr)
            except asyncio.TimeoutError:
                continue
            except Exception as e:
                logger.error(f"CSI collection error: {e}")
    
    async def process_csi_packet(self, data, addr):
        """Process incoming CSI packet"""
        try:
            csi_data = self.parse_csi_packet(data)
            await self.data_queue.put(csi_data)
        except Exception as e:
            logger.error(f"CSI parsing error: {e}")
```

#### 4.1.2 CSI Packet Parsing
**Atheros CSI Format**:
```python
class AtheriosCSIParser:
    def __init__(self):
        self.packet_format = struct.Struct('<HHHHH')  # Header format
        
    def parse_packet(self, raw_data):
        """Parse Atheros CSI packet format"""
        if len(raw_data) < 10:  # Minimum header size
            raise ValueError("Packet too short")
        
        # Parse header
        header = self.packet_format.unpack(raw_data[:10])
        timestamp, length, rate, channel, rssi = header
        
        # Extract CSI data
        csi_start = 10
        csi_length = length - 10
        csi_raw = raw_data[csi_start:csi_start + csi_length]
        
        # Parse complex CSI values
        csi_complex = self.parse_complex_csi(csi_raw)
        
        return {
            'timestamp': timestamp,
            'channel': channel,
            'rssi': rssi,
            'csi_data': csi_complex,
            'amplitude': np.abs(csi_complex),
            'phase': np.angle(csi_complex)
        }
    
    def parse_complex_csi(self, csi_raw):
        """Parse complex CSI values from raw bytes"""
        # Atheros format: 3x3 MIMO, 56 subcarriers
        num_subcarriers = 56
        num_antennas = 9  # 3x3 MIMO
        
        csi_complex = np.zeros((num_antennas, num_subcarriers), dtype=complex)
        
        for i in range(num_antennas):
            for j in range(num_subcarriers):
                idx = (i * num_subcarriers + j) * 4  # 4 bytes per complex value
                if idx + 4 <= len(csi_raw):
                    real = struct.unpack('<h', csi_raw[idx:idx+2])[0]
                    imag = struct.unpack('<h', csi_raw[idx+2:idx+4])[0]
                    csi_complex[i, j] = complex(real, imag)
        
        return csi_complex
```

### 4.2 Signal Processing Pipeline

#### 4.2.1 Real-Time Processing
**Streaming Data Processor**:
```python
class StreamingProcessor:
    def __init__(self, window_size=100):
        self.window_size = window_size
        self.data_buffer = collections.deque(maxlen=window_size)
        self.background_model = None
        
    async def process_stream(self, csi_data):
        """Process streaming CSI data"""
        # Add to buffer
        self.data_buffer.append(csi_data)
        
        if len(self.data_buffer) < self.window_size:
            return None  # Wait for sufficient data
        
        # Extract current window
        window_data = np.array(list(self.data_buffer))
        
        # Apply preprocessing
        processed_data = self.preprocess_window(window_data)
        
        # Background subtraction
        if self.background_model is not None:
            processed_data = processed_data - self.background_model
        
        return processed_data
    
    def preprocess_window(self, window_data):
        """Apply preprocessing to data window"""
        # Phase unwrapping
        phase_data = np.angle(window_data)
        unwrapped_phase = np.unwrap(phase_data, axis=-1)
        
        # Amplitude processing
        amplitude_data = np.abs(window_data)
        amplitude_db = 20 * np.log10(amplitude_data + 1e-10)
        
        # Temporal filtering
        filtered_amplitude = self.apply_temporal_filter(amplitude_db)
        filtered_phase = self.apply_temporal_filter(unwrapped_phase)
        
        # Combine amplitude and phase
        processed = np.stack([filtered_amplitude, filtered_phase], axis=-1)
        
        return processed
```

#### 4.2.2 Background Subtraction
**Adaptive Background Model**:
```python
class AdaptiveBackgroundModel:
    def __init__(self, learning_rate=0.01):
        self.learning_rate = learning_rate
        self.background = None
        self.variance = None
        
    def update_background(self, csi_data):
        """Update background model with new data"""
        if self.background is None:
            self.background = csi_data.copy()
            self.variance = np.ones_like(csi_data)
            return
        
        # Exponential moving average
        self.background = (1 - self.learning_rate) * self.background + \
                         self.learning_rate * csi_data
        
        # Update variance estimate
        diff = csi_data - self.background
        self.variance = (1 - self.learning_rate) * self.variance + \
                       self.learning_rate * (diff ** 2)
    
    def subtract_background(self, csi_data):
        """Subtract background from CSI data"""
        if self.background is None:
            return csi_data
        
        # Subtract background
        foreground = csi_data - self.background
        
        # Normalize by variance
        normalized = foreground / (np.sqrt(self.variance) + 1e-10)
        
        return normalized
```

---

## 5. API Implementation

### 5.1 REST API Architecture

#### 5.1.1 FastAPI Server Implementation
**Main Server Structure**:
```python
from fastapi import FastAPI, WebSocket, HTTPException
from fastapi.middleware.cors import CORSMiddleware
import asyncio

app = FastAPI(title="WiFi-DensePose API", version="1.0.0")

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Global state
pose_estimator = None
websocket_manager = WebSocketManager()

@app.on_event("startup")
async def startup_event():
    """Initialize system on startup"""
    global pose_estimator
    pose_estimator = PoseEstimator()
    await pose_estimator.initialize()

@app.get("/pose/latest")
async def get_latest_pose():
    """Get latest pose estimation results"""
    if pose_estimator is None:
        raise HTTPException(status_code=503, detail="System not initialized")
    
    latest_pose = await pose_estimator.get_latest_pose()
    if latest_pose is None:
        raise HTTPException(status_code=404, detail="No pose data available")
    
    return {
        "timestamp": latest_pose.timestamp,
        "persons": [person.to_dict() for person in latest_pose.persons],
        "metadata": latest_pose.metadata
    }

@app.get("/pose/history")
async def get_pose_history(
    start_time: Optional[datetime] = None,
    end_time: Optional[datetime] = None,
    limit: int = 100
):
    """Get historical pose data"""
    history = await pose_estimator.get_pose_history(
        start_time=start_time,
        end_time=end_time,
        limit=limit
    )
    
    return {
        "poses": [pose.to_dict() for pose in history],
        "count": len(history)
    }
```

#### 5.1.2 WebSocket Implementation
**Real-Time Streaming**:
```python
class WebSocketManager:
    def __init__(self):
        self.active_connections: List[WebSocket] = []
        self.connection_info: Dict[WebSocket, dict] = {}
    
    async def connect(self, websocket: WebSocket, client_info: dict):
        """Accept new WebSocket connection"""
        await websocket.accept()
        self.active_connections.append(websocket)
        self.connection_info[websocket] = client_info
        logger.info(f"Client connected: {client_info}")
    
    def disconnect(self, websocket: WebSocket):
        """Remove WebSocket connection"""
        if websocket in self.active_connections:
            self.active_connections.remove(websocket)
            del self.connection_info[websocket]
    
    async def broadcast_pose_data(self, pose_data: dict):
        """Broadcast pose data to all connected clients"""
        if not self.active_connections:
            return
        
        message = {
            "type": "pose_update",
            "data": pose_data,
            "timestamp": datetime.utcnow().isoformat()
        }
        
        # Send to all connections
        disconnected = []
        for connection in self.active_connections:
            try:
                await connection.send_json(message)
            except Exception as e:
                logger.error(f"WebSocket send error: {e}")
                disconnected.append(connection)
        
        # Clean up disconnected clients
        for connection in disconnected:
            self.disconnect(connection)

@app.websocket("/ws/pose")
async def websocket_pose_endpoint(websocket: WebSocket):
    """WebSocket endpoint for real-time pose data"""
    client_info = {
        "client_ip": websocket.client.host,
        "connect_time": datetime.utcnow()
    }
    
    await websocket_manager.connect(websocket, client_info)
    
    try:
        while True:
            # Keep connection alive and handle client messages
            data = await websocket.receive_text()
            # Process client commands if needed
            await handle_websocket_command(websocket, data)
    except Exception as e:
        logger.error(f"WebSocket error: {e}")
    finally:
        websocket_manager.disconnect(websocket)
```

### 5.2 External Integration APIs

#### 5.2.1 MQTT Integration
**MQTT Publisher Implementation**:
```python
import paho.mqtt.client as mqtt
import json

class MQTTPublisher:
    def __init__(self, broker_host, broker_port=1883):
        self.broker_host = broker_host
        self.broker_port = broker_port
        self.client = mqtt.Client()
        self.connected = False
        
    async def connect(self):
        """Connect to MQTT broker"""
        def on_connect(client, userdata, flags, rc):
            if rc == 0:
                self.connected = True
                logger.info("Connected to MQTT broker")
            else:
                logger.error(f"MQTT connection failed: {rc}")
        
        def on_disconnect(client, userdata, rc):
            self.connected = False
            logger.info("Disconnected from MQTT broker")
        
        self.client.on_connect = on_connect
        self.client.on_disconnect = on_disconnect
        
        try:
            self.client.connect(self.broker_host, self.broker_port, 60)
            self.client.loop_start()
        except Exception as e:
            logger.error(f"MQTT connection error: {e}")
    
    async def publish_pose_data(self, pose_data):
        """Publish pose data to MQTT topics"""
        if not self.connected:
            return
        
        # Publish individual person data
        for person in pose_data.persons:
            topic = f"wifi-densepose/pose/person/{person.id}"
            payload = {
                "id": person.id,
                "keypoints": person.keypoints,
                "confidence": person.confidence,
                "timestamp": pose_data.timestamp
            }
            
            self.client.publish(topic, json.dumps(payload))
        
        # Publish summary data
        summary_topic = "wifi-densepose/summary"
        summary_payload = {
            "person_count": len(pose_data.persons),
            "timestamp": pose_data.timestamp,
            "processing_time": pose_data.metadata.get("processing_time", 0)
        }
        
        self.client.publish(summary_topic, json.dumps(summary_payload))
```

#### 5.2.2 Webhook Integration
**Webhook Delivery System**:
```python
import aiohttp
import asyncio
from typing import List, Dict

class WebhookManager:
    def __init__(self):
        self.webhooks: List[Dict] = []
        self.session = None
        
    async def initialize(self):
        """Initialize HTTP session"""
        self.session = aiohttp.ClientSession()
    
    def add_webhook(self, url: str, events: List[str], auth: Dict = None):
        """Add webhook configuration"""
        webhook = {
            "url": url,
            "events": events,
            "auth": auth,
            "retry_count": 0,
            "max_retries": 3
        }
        self.webhooks.append(webhook)
    
    async def send_webhook(self, event_type: str, data: Dict):
        """Send webhook notifications for event"""
        relevant_webhooks = [
            wh for wh in self.webhooks 
            if event_type in wh["events"]
        ]
        
        tasks = []
        for webhook in relevant_webhooks:
            task = asyncio.create_task(
                self._deliver_webhook(webhook, event_type, data)
            )
            tasks.append(task)
        
        if tasks:
            await asyncio.gather(*tasks, return_exceptions=True)
    
    async def _deliver_webhook(self, webhook: Dict, event_type: str, data: Dict):
        """Deliver individual webhook with retry logic"""
        payload = {
            "event": event_type,
            "timestamp": datetime.utcnow().isoformat(),
            "data": data
        }
        
        headers = {"Content-Type": "application/json"}
        
        # Add authentication if configured
        if webhook.get("auth"):
            auth = webhook["auth"]
            if auth.get("type") == "bearer":
                headers["Authorization"] = f"Bearer {auth['token']}"
            elif auth.get("type") == "basic":
                # Handle basic auth
                pass
        
        for attempt in range(webhook["max_retries"]):
            try:
                async with self.session.post(
                    webhook["url"],
                    json=payload,
                    headers=headers,
                    timeout=aiohttp.ClientTimeout(total=10)
                ) as response:
                    if response.status < 400:
                        logger.info(f"Webhook delivered: {webhook['url']}")
                        return
                    else:
                        logger.warning(f"Webhook failed: {response.status}")
                        
            except Exception as e:

logger.error(f"Webhook delivery failed: {e}")
                await asyncio.sleep(2 ** attempt)  # Exponential backoff
        
        logger.error(f"Webhook delivery failed after {webhook['max_retries']} attempts")
```

---

## 6. Performance Requirements and Optimization

### 6.1 System Performance Specifications

#### 6.1.1 Processing Performance
**Real-Time Processing Requirements**:
- **End-to-End Latency**: <100ms (95th percentile)
- **Processing Throughput**: 10-30 FPS depending on hardware configuration
- **Memory Usage**: <4GB RAM for standard operation
- **GPU Memory**: <2GB VRAM for neural network inference

**Performance Scaling**:
```python
class PerformanceManager:
    def __init__(self):
        self.performance_targets = {
            "cpu_only": {"fps": 10, "latency_ms": 150},
            "gpu_basic": {"fps": 20, "latency_ms": 100},
            "gpu_high_end": {"fps": 30, "latency_ms": 75}
        }
        
    def detect_hardware_capability(self):
        """Detect available hardware and set performance targets"""
        if torch.cuda.is_available():
            gpu_memory = torch.cuda.get_device_properties(0).total_memory
            if gpu_memory > 8e9:  # 8GB+
                return "gpu_high_end"
            else:
                return "gpu_basic"
        return "cpu_only"
    
    def optimize_for_hardware(self, capability):
        """Optimize processing pipeline for detected hardware"""
        targets = self.performance_targets[capability]
        
        # Adjust batch sizes
        if capability == "gpu_high_end":
            self.batch_size = 8
            self.model_precision = torch.float16
        elif capability == "gpu_basic":
            self.batch_size = 4
            self.model_precision = torch.float32
        else:
            self.batch_size = 1
            self.model_precision = torch.float32
```

// TEST: Verify performance targets are met on different hardware configurations
// TEST: Confirm automatic hardware detection and optimization
// TEST: Validate memory usage stays within specified limits

#### 6.1.2 Scalability Requirements
**Concurrent Processing**: Support multiple simultaneous operations
- **API Requests**: 1000 concurrent REST API requests
- **WebSocket Connections**: 100 simultaneous streaming clients
- **Data Processing**: Parallel CSI stream processing
- **Storage Operations**: Concurrent read/write operations

**Resource Management**:
```python
class ResourceManager:
    def __init__(self, max_memory_gb=4, max_gpu_memory_gb=2):
        self.max_memory = max_memory_gb * 1e9
        self.max_gpu_memory = max_gpu_memory_gb * 1e9
        self.memory_monitor = MemoryMonitor()
        
    async def monitor_resources(self):
        """Continuously monitor system resources"""
        while True:
            memory_usage = self.memory_monitor.get_memory_usage()
            gpu_usage = self.memory_monitor.get_gpu_memory_usage()
            
            if memory_usage > 0.9 * self.max_memory:
                await self.trigger_memory_cleanup()
            
            if gpu_usage > 0.9 * self.max_gpu_memory:
                await self.trigger_gpu_cleanup()
            
            await asyncio.sleep(5)  # Check every 5 seconds
    
    async def trigger_memory_cleanup(self):
        """Trigger memory cleanup procedures"""
        # Clear data buffers
        self.data_buffer.clear_old_entries()
        # Force garbage collection
        gc.collect()
        # Reduce batch sizes temporarily
        self.reduce_batch_sizes()
```

// TEST: Verify system handles specified concurrent load
// TEST: Confirm resource monitoring prevents memory exhaustion
// TEST: Validate automatic resource cleanup procedures

### 6.2 Neural Network Optimization

#### 6.2.1 Model Optimization Techniques
**Quantization**: Reduce model size and improve inference speed
```python
class ModelOptimizer:
    def __init__(self, model):
        self.model = model
        
    def apply_quantization(self, quantization_type="dynamic"):
        """Apply quantization to reduce model size"""
        if quantization_type == "dynamic":
            # Dynamic quantization for CPU inference
            quantized_model = torch.quantization.quantize_dynamic(
                self.model, 
                {torch.nn.Linear, torch.nn.Conv2d}, 
                dtype=torch.qint8
            )
        elif quantization_type == "static":
            # Static quantization for better performance
            quantized_model = self.apply_static_quantization()
        
        return quantized_model
    
    def apply_pruning(self, sparsity=0.3):
        """Apply structured pruning to reduce model complexity"""
        import torch.nn.utils.prune as prune
        
        for module in self.model.modules():
            if isinstance(module, torch.nn.Conv2d):
                prune.l1_unstructured(module, name='weight', amount=sparsity)
        
        return self.model
```

// TEST: Verify quantization maintains accuracy while improving speed
// TEST: Confirm pruning reduces model size without significant accuracy loss
// TEST: Validate optimization techniques work on target hardware

#### 6.2.2 Inference Optimization
**Batch Processing**: Optimize throughput with intelligent batching
```python
class InferenceBatcher:
    def __init__(self, max_batch_size=8, max_wait_time=0.01):
        self.max_batch_size = max_batch_size
        self.max_wait_time = max_wait_time
        self.pending_requests = []
        self.batch_timer = None
        
    async def add_request(self, csi_data, callback):
        """Add inference request to batch"""
        request = {
            'data': csi_data,
            'callback': callback,
            'timestamp': time.time()
        }
        
        self.pending_requests.append(request)
        
        if len(self.pending_requests) >= self.max_batch_size:
            await self.process_batch()
        elif self.batch_timer is None:
            self.batch_timer = asyncio.create_task(
                self.wait_and_process()
            )
    
    async def process_batch(self):
        """Process current batch of requests"""
        if not self.pending_requests:
            return
        
        # Extract data and callbacks
        batch_data = [req['data'] for req in self.pending_requests]
        callbacks = [req['callback'] for req in self.pending_requests]
        
        # Process batch
        batch_tensor = torch.stack(batch_data)
        with torch.no_grad():
            batch_results = self.model(batch_tensor)
        
        # Return results to callbacks
        for i, callback in enumerate(callbacks):
            await callback(batch_results[i])
        
        # Clear processed requests
        self.pending_requests.clear()
```

// TEST: Verify batch processing improves overall throughput
// TEST: Confirm batching maintains acceptable latency
// TEST: Validate batch timer prevents indefinite waiting

### 6.3 Hardware Interface Optimization

#### 6.3.1 CSI Data Processing Optimization
**Parallel Processing**: Optimize CSI data collection and processing
```python
class OptimizedCSIProcessor:
    def __init__(self, num_workers=4):
        self.num_workers = num_workers
        self.processing_pool = ProcessPoolExecutor(max_workers=num_workers)
        self.data_queue = asyncio.Queue(maxsize=1000)
        
    async def start_processing(self):
        """Start parallel CSI processing workers"""
        tasks = []
        for i in range(self.num_workers):
            task = asyncio.create_task(self.processing_worker(i))
            tasks.append(task)
        
        await asyncio.gather(*tasks)
    
    def process_csi_data(self, csi_data):
        """CPU-intensive CSI processing in separate process"""
        # Phase unwrapping
        phase_unwrapped = np.unwrap(np.angle(csi_data), axis=-1)
        
        # Amplitude processing
        amplitude_db = 20 * np.log10(np.abs(csi_data) + 1e-10)
        
        # Apply filters using optimized NumPy operations
        filtered_phase = scipy.signal.savgol_filter(phase_unwrapped, 5, 2, axis=-1)
        filtered_amplitude = scipy.signal.savgol_filter(amplitude_db, 5, 2, axis=-1)
        
        # Combine and normalize
        processed = np.stack([filtered_amplitude, filtered_phase], axis=-1)
        normalized = (processed - processed.mean()) / (processed.std() + 1e-10)
        
        return normalized
```

// TEST: Verify parallel processing improves CSI data throughput
// TEST: Confirm worker processes handle errors gracefully
// TEST: Validate processed data quality meets neural network requirements

---

## 7. Deployment and Infrastructure

### 7.1 Container Architecture

#### 7.1.1 Docker Configuration
**Multi-Stage Build**: Optimize container size and security
```dockerfile
# Build stage
FROM python:3.9-slim as builder

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir --user -r requirements.txt

# Production stage
FROM python:3.9-slim

# Install system dependencies
RUN apt-get update && apt-get install -y \
    libgl1-mesa-glx \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender-dev \
    libgomp1 \
    && rm -rf /var/lib/apt/lists/*

# Copy Python packages from builder
COPY --from=builder /root/.local /root/.local
ENV PATH=/root/.local/bin:$PATH

# Copy application code
WORKDIR /app
COPY . .

# Create non-root user
RUN useradd -m -u 1000 wifipose && \
    chown -R wifipose:wifipose /app
USER wifipose

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
    CMD curl -f http://localhost:8000/health || exit 1

EXPOSE 8000
CMD ["python", "-m", "wifi_densepose.main"]
```

#### 7.1.2 Kubernetes Deployment
**Production Deployment Configuration**:
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: wifi-densepose
  labels:
    app: wifi-densepose
spec:
  replicas: 3
  selector:
    matchLabels:
      app: wifi-densepose
  template:
    metadata:
      labels:
        app: wifi-densepose
    spec:
      containers:
      - name: wifi-densepose
        image: wifi-densepose:latest
        ports:
        - containerPort: 8000
        env:
        - name: CUDA_VISIBLE_DEVICES
          value: "0"
        - name: LOG_LEVEL
          value: "INFO"
        resources:
          requests:
            memory: "2Gi"
            cpu: "1000m"
            nvidia.com/gpu: 1
          limits:
            memory: "4Gi"
            cpu: "2000m"
            nvidia.com/gpu: 1
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 60
          periodSeconds: 30
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 10
```

// TEST: Verify Docker container builds and runs correctly
// TEST: Confirm Kubernetes deployment scales properly
// TEST: Validate health checks and resource limits

### 7.2 Monitoring and Observability

#### 7.2.1 Metrics Collection
**Prometheus Integration**: Comprehensive metrics collection
```python
from prometheus_client import Counter, Histogram, Gauge, start_http_server

class MetricsCollector:
    def __init__(self):
        # Performance metrics
        self.inference_duration = Histogram(
            'inference_duration_seconds',
            'Time spent on neural network inference',
            buckets=[0.01, 0.025, 0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1.0]
        )
        
        self.pose_detection_count = Counter(
            'pose_detections_total',
            'Total number of pose detections',
            ['confidence_level']
        )
        
        self.active_persons = Gauge(
            'active_persons_current',
            'Current number of tracked persons'
        )
        
        # System metrics
        self.memory_usage = Gauge(
            'memory_usage_bytes',
            'Current memory usage in bytes'
        )
        
        self.gpu_utilization = Gauge(
            'gpu_utilization_percent',
            'GPU utilization percentage'
        )
    
    def record_inference_time(self, duration):
        """Record neural network inference time"""
        self.inference_duration.observe(duration)
    
    def start_metrics_server(self, port=8001):
        """Start Prometheus metrics server"""
        start_http_server(port)
        logger.info(f"Metrics server started on port {port}")
```

// TEST: Verify metrics collection captures all key performance indicators
// TEST: Confirm Prometheus integration works correctly
// TEST: Validate metrics provide actionable insights

---

## 8. Security and Compliance

### 8.1 Data Security

#### 8.1.1 Privacy-Preserving Design
**Data Minimization**: Collect only necessary data for pose estimation
```python
class PrivacyPreservingProcessor:
    def __init__(self):
        self.data_retention_days = 7  # Configurable retention period
        self.anonymization_enabled = True
        
    def process_pose_data(self, raw_poses):
        """Process poses with privacy preservation"""
        if self.anonymization_enabled:
            # Remove personally identifiable features
            anonymized_poses = self.anonymize_poses(raw_poses)
            return anonymized_poses
        return raw_poses
    
    def anonymize_poses(self, poses):
        """Remove identifying characteristics from pose data"""
        anonymized = []
        
        for pose in poses:
            # Remove fine-grained features that could identify individuals
            anonymized_pose = {
                'keypoints': self.generalize_keypoints(pose['keypoints']),
                'confidence': pose['confidence'],
                'timestamp': pose['timestamp'],
                'activity': pose.get('activity', 'unknown')
            }
            anonymized.append(anonymized_pose)
        
        return anonymized
    
    async def cleanup_old_data(self):
        """Automatically delete old data based on retention policy"""
        cutoff_date = datetime.now() - timedelta(days=self.data_retention_days)
        
        # Delete old pose data
        await self.database.delete_poses_before(cutoff_date)
        
        # Delete old CSI data
        await self.database.delete_csi_before(cutoff_date)
        
        logger.info(f"Cleaned up data older than {cutoff_date}")
```

// TEST: Verify anonymization removes identifying characteristics
// TEST: Confirm data retention policies are enforced automatically
// TEST: Validate privacy preservation doesn't impact functionality

---

## 9. Testing and Quality Assurance

### 9.1 London School TDD Implementation

#### 9.1.1 Test-First Development
**Comprehensive Test Coverage**: Following London School TDD principles
```python
import pytest
import asyncio
from unittest.mock import Mock, AsyncMock, patch
import numpy as np
import torch

class TestPoseEstimationPipeline:
    """Test suite following London School TDD principles"""
    
    @pytest.fixture
    def mock_csi_data(self):
        """Generate synthetic CSI data for testing"""
        return np.random.complex128((3, 3, 56, 100))  # 3x3 MIMO, 56 subcarriers, 100 samples
    
    @pytest.fixture
    def mock_neural_network(self):
        """Mock neural network for isolated testing"""
        mock_network = Mock()
        mock_network.forward.return_value = torch.randn(1, 17, 3)  # Mock pose output
        return mock_network
    
    async def test_csi_preprocessing_pipeline(self, mock_csi_data):
        """Test CSI preprocessing produces valid output"""
        # Arrange
        processor = CSIProcessor()
        
        # Act
        processed_data = await processor.preprocess(mock_csi_data)
        
        # Assert
        assert processed_data.shape == (3, 3, 56, 100)
        assert not np.isnan(processed_data).any()
        assert not np.isinf(processed_data).any()
        
        # Verify phase unwrapping
        phase_data = np.angle(processed_data)
        phase_diff = np.diff(phase_data, axis=-1)
        assert np.abs(phase_diff).max() < np.pi  # No phase jumps > π
    
    async def test_neural_network_inference_performance(self, mock_csi_data, mock_neural_network):
        """Test neural network inference meets performance requirements"""
        # Arrange
        estimator = PoseEstimator()
        estimator.neural_network = mock_neural_network
        
        # Act
        start_time = time.time()
        result = await estimator.neural_inference(mock_csi_data)
        inference_time = time.time() - start_time
        
        # Assert
        assert inference_time < 0.05  # <50ms requirement
        assert result is not None
        mock_neural_network.forward.assert_called_once()
    
    async def test_fall_detection_accuracy(self):
        """Test fall detection algorithm accuracy"""
        # Arrange
        fall_detector = FallDetector()
        
        # Simulate fall trajectory
        fall_trajectory = [
            {'position': np.array([100, 100]), 'timestamp': 0.0},    # Standing
            {'position': np.array([100, 120]), 'timestamp': 0.5},    # Falling
            {'position': np.array([100, 180]), 'timestamp': 1.0},    # On ground
            {'position': np.array([100, 180]), 'timestamp': 1.5},    # Still on ground
        ]
        
        # Act
        fall_detected = False
        for pose in fall_trajectory:
            result = fall_detector.analyze_pose(pose)
            if result['fall_detected']:
                fall_detected = True
                break
        
        # Assert
        assert fall_detected
        assert result['confidence'] > 0.8
```

// TEST: Verify all test cases pass with >95% coverage
// TEST: Confirm TDD approach catches regressions early
// TEST: Validate integration tests cover real-world scenarios

---

## 10. Acceptance Criteria

### 10.1 Technical Implementation Criteria
- **CSI Processing Pipeline**: Real-time CSI data collection and preprocessing functional
- **Neural Network Integration**: DensePose model integration with <50ms inference time
- **Multi-Person Tracking**: Robust tracking of up to 5 individuals simultaneously
- **API Implementation**: Complete REST and WebSocket API implementation
- **Performance Targets**: All latency and throughput requirements met

### 10.2 Integration Criteria
- **Hardware Integration**: Successful integration with WiFi routers and CSI extraction
- **External Service Integration**: MQTT, webhook, and Restream integrations operational
- **Database Integration**: Efficient data storage and retrieval implementation
- **Monitoring Integration**: Comprehensive system monitoring and alerting

### 10.3 Quality Assurance Criteria
- **Test Coverage**: >90% unit test coverage, complete integration test suite
- **Performance Validation**: All performance benchmarks met under load testing
- **Security Validation**: Security measures tested and vulnerabilities addressed
- **Documentation Completeness**: Technical documentation complete and accurate

// TEST: Verify all technical implementation criteria are met
// TEST: Confirm integration criteria are satisfied
// TEST: Validate quality assurance criteria through comprehensive testing
