Technical Specification
#WiFi-DensePose System
- 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:
1class ModalityTranslationNetwork(nn.Module):
2 def __init__(self):
3 # Amplitude branch encoder
4 self.amplitude_encoder = nn.Sequential(
5 nn.Conv1d(9, 64, kernel_size=3, padding=1),
6 nn.BatchNorm1d(64),
7 nn.ReLU(),
8 nn.Conv1d(64, 128, kernel_size=3, padding=1),
9 nn.BatchNorm1d(128),
10 nn.ReLU(),
11 nn.AdaptiveAvgPool1d(256)
12 )
13
14 # Phase branch encoder
15 self.phase_encoder = nn.Sequential(
16 nn.Conv1d(9, 64, kernel_size=3, padding=1),
17 nn.BatchNorm1d(64),
18 nn.ReLU(),
19 nn.Conv1d(64, 128, kernel_size=3, padding=1),
20 nn.BatchNorm1d(128),
21 nn.ReLU(),
22 nn.AdaptiveAvgPool1d(256)
23 )
24
25 # Feature fusion and upsampling
26 self.fusion_network = nn.Sequential(
27 nn.Linear(512, 1024),
28 nn.ReLU(),
29 nn.Linear(1024, 720*1280*3),
30 nn.Sigmoid()
31 )
3.1.2 CSI Preprocessing Pipeline
Phase Unwrapping Algorithm:
1def unwrap_phase(phase_data):
2 """
3 Unwrap CSI phase data to remove 2π discontinuities
4 """
5 unwrapped = np.unwrap(phase_data, axis=-1)
6 # Apply linear detrending
7 detrended = signal.detrend(unwrapped, axis=-1)
8 # Temporal filtering
9 filtered = apply_moving_average(detrended, window=5)
10 return filtered
11
12def apply_moving_average(data, window=5):
13 """
14 Apply moving average filter for noise reduction
15 """
16 kernel = np.ones(window) / window
17 return np.convolve(data, kernel, mode='same')
Amplitude Processing:
1def process_amplitude(amplitude_data):
2 """
3 Process CSI amplitude data for neural network input
4 """
5 # Convert to dB scale
6 amplitude_db = 20 * np.log10(np.abs(amplitude_data) + 1e-10)
7 # Normalize to [0, 1] range
8 normalized = (amplitude_db - amplitude_db.min()) / (amplitude_db.max() - amplitude_db.min())
9 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:
1class TransferLearningFramework:
2 def __init__(self):
3 self.teacher_model = load_pretrained_densepose()
4 self.student_model = WiFiDensePoseModel()
5 self.translation_network = ModalityTranslationNetwork()
6
7 def knowledge_distillation_loss(self, wifi_features, image_features):
8 """
9 Compute knowledge distillation loss between teacher and student
10 """
11 teacher_output = self.teacher_model(image_features)
12 student_output = self.student_model(wifi_features)
13
14 # Feature matching loss
15 feature_loss = F.mse_loss(student_output.features, teacher_output.features)
16
17 # Pose estimation loss
18 pose_loss = F.cross_entropy(student_output.poses, teacher_output.poses)
19
20 return feature_loss + pose_loss
#3.3 Multi-Person Tracking
3.3.1 Tracking Algorithm
Hungarian Algorithm Implementation:
1class MultiPersonTracker:
2 def __init__(self, max_persons=5):
3 self.max_persons = max_persons
4 self.active_tracks = {}
5 self.next_id = 1
6
7 def update(self, detections):
8 """
9 Update tracks with new detections using Hungarian algorithm
10 """
11 if not self.active_tracks:
12 # Initialize tracks for first frame
13 return self.initialize_tracks(detections)
14
15 # Compute cost matrix
16 cost_matrix = self.compute_cost_matrix(detections)
17
18 # Solve assignment problem
19 assignments = self.hungarian_assignment(cost_matrix)
20
21 # Update tracks
22 return self.update_tracks(detections, assignments)
23
24 def compute_cost_matrix(self, detections):
25 """
26 Compute cost matrix for track-detection assignment
27 """
28 costs = np.zeros((len(self.active_tracks), len(detections)))
29
30 for i, track in enumerate(self.active_tracks.values()):
31 for j, detection in enumerate(detections):
32 # Compute distance-based cost
33 distance = np.linalg.norm(track.position - detection.position)
34 # Add appearance similarity cost
35 appearance_cost = 1 - self.compute_appearance_similarity(track, detection)
36 costs[i, j] = distance + appearance_cost
37
38 return costs
3.3.2 Kalman Filtering
State Prediction Model:
1class KalmanTracker:
2 def __init__(self):
3 # State vector: [x, y, vx, vy, ax, ay]
4 self.state = np.zeros(6)
5
6 # State transition matrix
7 self.F = np.array([
8 [1, 0, 1, 0, 0.5, 0],
9 [0, 1, 0, 1, 0, 0.5],
10 [0, 0, 1, 0, 1, 0],
11 [0, 0, 0, 1, 0, 1],
12 [0, 0, 0, 0, 1, 0],
13 [0, 0, 0, 0, 0, 1]
14 ])
15
16 # Measurement matrix
17 self.H = np.array([
18 [1, 0, 0, 0, 0, 0],
19 [0, 1, 0, 0, 0, 0]
20 ])
21
22 # Process and measurement noise
23 self.Q = np.eye(6) * 0.1 # Process noise
24 self.R = np.eye(2) * 1.0 # Measurement noise
25 self.P = np.eye(6) * 100 # Initial covariance
26
27 def predict(self):
28 """Predict next state"""
29 self.state = self.F @ self.state
30 self.P = self.F @ self.P @ self.F.T + self.Q
31 return self.state[:2] # Return position
32
33 def update(self, measurement):
34 """Update state with measurement"""
35 y = measurement - self.H @ self.state
36 S = self.H @ self.P @ self.H.T + self.R
37 K = self.P @ self.H.T @ np.linalg.inv(S)
38
39 self.state = self.state + K @ y
40 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:
1class CSIReceiver:
2 def __init__(self, port=5500, buffer_size=1024):
3 self.port = port
4 self.buffer_size = buffer_size
5 self.socket = None
6 self.running = False
7
8 async def start_collection(self):
9 """Start CSI data collection"""
10 self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
11 self.socket.bind(('0.0.0.0', self.port))
12 self.socket.setblocking(False)
13 self.running = True
14
15 while self.running:
16 try:
17 data, addr = await asyncio.wait_for(
18 self.socket.recvfrom(self.buffer_size),
19 timeout=1.0
20 )
21 await self.process_csi_packet(data, addr)
22 except asyncio.TimeoutError:
23 continue
24 except Exception as e:
25 logger.error(f"CSI collection error: {e}")
26
27 async def process_csi_packet(self, data, addr):
28 """Process incoming CSI packet"""
29 try:
30 csi_data = self.parse_csi_packet(data)
31 await self.data_queue.put(csi_data)
32 except Exception as e:
33 logger.error(f"CSI parsing error: {e}")
4.1.2 CSI Packet Parsing
Atheros CSI Format:
1class AtheriosCSIParser:
2 def __init__(self):
3 self.packet_format = struct.Struct('<HHHHH') # Header format
4
5 def parse_packet(self, raw_data):
6 """Parse Atheros CSI packet format"""
7 if len(raw_data) < 10: # Minimum header size
8 raise ValueError("Packet too short")
9
10 # Parse header
11 header = self.packet_format.unpack(raw_data[:10])
12 timestamp, length, rate, channel, rssi = header
13
14 # Extract CSI data
15 csi_start = 10
16 csi_length = length - 10
17 csi_raw = raw_data[csi_start:csi_start + csi_length]
18
19 # Parse complex CSI values
20 csi_complex = self.parse_complex_csi(csi_raw)
21
22 return {
23 'timestamp': timestamp,
24 'channel': channel,
25 'rssi': rssi,
26 'csi_data': csi_complex,
27 'amplitude': np.abs(csi_complex),
28 'phase': np.angle(csi_complex)
29 }
30
31 def parse_complex_csi(self, csi_raw):
32 """Parse complex CSI values from raw bytes"""
33 # Atheros format: 3x3 MIMO, 56 subcarriers
34 num_subcarriers = 56
35 num_antennas = 9 # 3x3 MIMO
36
37 csi_complex = np.zeros((num_antennas, num_subcarriers), dtype=complex)
38
39 for i in range(num_antennas):
40 for j in range(num_subcarriers):
41 idx = (i * num_subcarriers + j) * 4 # 4 bytes per complex value
42 if idx + 4 <= len(csi_raw):
43 real = struct.unpack('<h', csi_raw[idx:idx+2])[0]
44 imag = struct.unpack('<h', csi_raw[idx+2:idx+4])[0]
45 csi_complex[i, j] = complex(real, imag)
46
47 return csi_complex
#4.2 Signal Processing Pipeline
4.2.1 Real-Time Processing
Streaming Data Processor:
1class StreamingProcessor:
2 def __init__(self, window_size=100):
3 self.window_size = window_size
4 self.data_buffer = collections.deque(maxlen=window_size)
5 self.background_model = None
6
7 async def process_stream(self, csi_data):
8 """Process streaming CSI data"""
9 # Add to buffer
10 self.data_buffer.append(csi_data)
11
12 if len(self.data_buffer) < self.window_size:
13 return None # Wait for sufficient data
14
15 # Extract current window
16 window_data = np.array(list(self.data_buffer))
17
18 # Apply preprocessing
19 processed_data = self.preprocess_window(window_data)
20
21 # Background subtraction
22 if self.background_model is not None:
23 processed_data = processed_data - self.background_model
24
25 return processed_data
26
27 def preprocess_window(self, window_data):
28 """Apply preprocessing to data window"""
29 # Phase unwrapping
30 phase_data = np.angle(window_data)
31 unwrapped_phase = np.unwrap(phase_data, axis=-1)
32
33 # Amplitude processing
34 amplitude_data = np.abs(window_data)
35 amplitude_db = 20 * np.log10(amplitude_data + 1e-10)
36
37 # Temporal filtering
38 filtered_amplitude = self.apply_temporal_filter(amplitude_db)
39 filtered_phase = self.apply_temporal_filter(unwrapped_phase)
40
41 # Combine amplitude and phase
42 processed = np.stack([filtered_amplitude, filtered_phase], axis=-1)
43
44 return processed
4.2.2 Background Subtraction
Adaptive Background Model:
1class AdaptiveBackgroundModel:
2 def __init__(self, learning_rate=0.01):
3 self.learning_rate = learning_rate
4 self.background = None
5 self.variance = None
6
7 def update_background(self, csi_data):
8 """Update background model with new data"""
9 if self.background is None:
10 self.background = csi_data.copy()
11 self.variance = np.ones_like(csi_data)
12 return
13
14 # Exponential moving average
15 self.background = (1 - self.learning_rate) * self.background + \
16 self.learning_rate * csi_data
17
18 # Update variance estimate
19 diff = csi_data - self.background
20 self.variance = (1 - self.learning_rate) * self.variance + \
21 self.learning_rate * (diff ** 2)
22
23 def subtract_background(self, csi_data):
24 """Subtract background from CSI data"""
25 if self.background is None:
26 return csi_data
27
28 # Subtract background
29 foreground = csi_data - self.background
30
31 # Normalize by variance
32 normalized = foreground / (np.sqrt(self.variance) + 1e-10)
33
34 return normalized
#5. API Implementation
#5.1 REST API Architecture
5.1.1 FastAPI Server Implementation
Main Server Structure:
1from fastapi import FastAPI, WebSocket, HTTPException
2from fastapi.middleware.cors import CORSMiddleware
3import asyncio
4
5app = FastAPI(title="WiFi-DensePose API", version="1.0.0")
6
7# CORS middleware
8app.add_middleware(
9 CORSMiddleware,
10 allow_origins=["*"],
11 allow_credentials=True,
12 allow_methods=["*"],
13 allow_headers=["*"],
14)
15
16# Global state
17pose_estimator = None
18websocket_manager = WebSocketManager()
19
20@app.on_event("startup")
21async def startup_event():
22 """Initialize system on startup"""
23 global pose_estimator
24 pose_estimator = PoseEstimator()
25 await pose_estimator.initialize()
26
27@app.get("/pose/latest")
28async def get_latest_pose():
29 """Get latest pose estimation results"""
30 if pose_estimator is None:
31 raise HTTPException(status_code=503, detail="System not initialized")
32
33 latest_pose = await pose_estimator.get_latest_pose()
34 if latest_pose is None:
35 raise HTTPException(status_code=404, detail="No pose data available")
36
37 return {
38 "timestamp": latest_pose.timestamp,
39 "persons": [person.to_dict() for person in latest_pose.persons],
40 "metadata": latest_pose.metadata
41 }
42
43@app.get("/pose/history")
44async def get_pose_history(
45 start_time: Optional[datetime] = None,
46 end_time: Optional[datetime] = None,
47 limit: int = 100
48):
49 """Get historical pose data"""
50 history = await pose_estimator.get_pose_history(
51 start_time=start_time,
52 end_time=end_time,
53 limit=limit
54 )
55
56 return {
57 "poses": [pose.to_dict() for pose in history],
58 "count": len(history)
59 }
5.1.2 WebSocket Implementation
Real-Time Streaming:
1class WebSocketManager:
2 def __init__(self):
3 self.active_connections: List[WebSocket] = []
4 self.connection_info: Dict[WebSocket, dict] = {}
5
6 async def connect(self, websocket: WebSocket, client_info: dict):
7 """Accept new WebSocket connection"""
8 await websocket.accept()
9 self.active_connections.append(websocket)
10 self.connection_info[websocket] = client_info
11 logger.info(f"Client connected: {client_info}")
12
13 def disconnect(self, websocket: WebSocket):
14 """Remove WebSocket connection"""
15 if websocket in self.active_connections:
16 self.active_connections.remove(websocket)
17 del self.connection_info[websocket]
18
19 async def broadcast_pose_data(self, pose_data: dict):
20 """Broadcast pose data to all connected clients"""
21 if not self.active_connections:
22 return
23
24 message = {
25 "type": "pose_update",
26 "data": pose_data,
27 "timestamp": datetime.utcnow().isoformat()
28 }
29
30 # Send to all connections
31 disconnected = []
32 for connection in self.active_connections:
33 try:
34 await connection.send_json(message)
35 except Exception as e:
36 logger.error(f"WebSocket send error: {e}")
37 disconnected.append(connection)
38
39 # Clean up disconnected clients
40 for connection in disconnected:
41 self.disconnect(connection)
42
43@app.websocket("/ws/pose")
44async def websocket_pose_endpoint(websocket: WebSocket):
45 """WebSocket endpoint for real-time pose data"""
46 client_info = {
47 "client_ip": websocket.client.host,
48 "connect_time": datetime.utcnow()
49 }
50
51 await websocket_manager.connect(websocket, client_info)
52
53 try:
54 while True:
55 # Keep connection alive and handle client messages
56 data = await websocket.receive_text()
57 # Process client commands if needed
58 await handle_websocket_command(websocket, data)
59 except Exception as e:
60 logger.error(f"WebSocket error: {e}")
61 finally:
62 websocket_manager.disconnect(websocket)
#5.2 External Integration APIs
5.2.1 MQTT Integration
MQTT Publisher Implementation:
1import paho.mqtt.client as mqtt
2import json
3
4class MQTTPublisher:
5 def __init__(self, broker_host, broker_port=1883):
6 self.broker_host = broker_host
7 self.broker_port = broker_port
8 self.client = mqtt.Client()
9 self.connected = False
10
11 async def connect(self):
12 """Connect to MQTT broker"""
13 def on_connect(client, userdata, flags, rc):
14 if rc == 0:
15 self.connected = True
16 logger.info("Connected to MQTT broker")
17 else:
18 logger.error(f"MQTT connection failed: {rc}")
19
20 def on_disconnect(client, userdata, rc):
21 self.connected = False
22 logger.info("Disconnected from MQTT broker")
23
24 self.client.on_connect = on_connect
25 self.client.on_disconnect = on_disconnect
26
27 try:
28 self.client.connect(self.broker_host, self.broker_port, 60)
29 self.client.loop_start()
30 except Exception as e:
31 logger.error(f"MQTT connection error: {e}")
32
33 async def publish_pose_data(self, pose_data):
34 """Publish pose data to MQTT topics"""
35 if not self.connected:
36 return
37
38 # Publish individual person data
39 for person in pose_data.persons:
40 topic = f"wifi-densepose/pose/person/{person.id}"
41 payload = {
42 "id": person.id,
43 "keypoints": person.keypoints,
44 "confidence": person.confidence,
45 "timestamp": pose_data.timestamp
46 }
47
48 self.client.publish(topic, json.dumps(payload))
49
50 # Publish summary data
51 summary_topic = "wifi-densepose/summary"
52 summary_payload = {
53 "person_count": len(pose_data.persons),
54 "timestamp": pose_data.timestamp,
55 "processing_time": pose_data.metadata.get("processing_time", 0)
56 }
57
58 self.client.publish(summary_topic, json.dumps(summary_payload))
5.2.2 Webhook Integration
Webhook Delivery System:
1import aiohttp
2import asyncio
3from typing import List, Dict
4
5class WebhookManager:
6 def __init__(self):
7 self.webhooks: List[Dict] = []
8 self.session = None
9
10 async def initialize(self):
11 """Initialize HTTP session"""
12 self.session = aiohttp.ClientSession()
13
14 def add_webhook(self, url: str, events: List[str], auth: Dict = None):
15 """Add webhook configuration"""
16 webhook = {
17 "url": url,
18 "events": events,
19 "auth": auth,
20 "retry_count": 0,
21 "max_retries": 3
22 }
23 self.webhooks.append(webhook)
24
25 async def send_webhook(self, event_type: str, data: Dict):
26 """Send webhook notifications for event"""
27 relevant_webhooks = [
28 wh for wh in self.webhooks
29 if event_type in wh["events"]
30 ]
31
32 tasks = []
33 for webhook in relevant_webhooks:
34 task = asyncio.create_task(
35 self._deliver_webhook(webhook, event_type, data)
36 )
37 tasks.append(task)
38
39 if tasks:
40 await asyncio.gather(*tasks, return_exceptions=True)
41
42 async def _deliver_webhook(self, webhook: Dict, event_type: str, data: Dict):
43 """Deliver individual webhook with retry logic"""
44 payload = {
45 "event": event_type,
46 "timestamp": datetime.utcnow().isoformat(),
47 "data": data
48 }
49
50 headers = {"Content-Type": "application/json"}
51
52 # Add authentication if configured
53 if webhook.get("auth"):
54 auth = webhook["auth"]
55 if auth.get("type") == "bearer":
56 headers["Authorization"] = f"Bearer {auth['token']}"
57 elif auth.get("type") == "basic":
58 # Handle basic auth
59 pass
60
61 for attempt in range(webhook["max_retries"]):
62 try:
63 async with self.session.post(
64 webhook["url"],
65 json=payload,
66 headers=headers,
67 timeout=aiohttp.ClientTimeout(total=10)
68 ) as response:
69 if response.status < 400:
70 logger.info(f"Webhook delivered: {webhook['url']}")
71 return
72 else:
73 logger.warning(f"Webhook failed: {response.status}")
74
75 except Exception as e:
76
77logger.error(f"Webhook delivery failed: {e}")
78 await asyncio.sleep(2 ** attempt) # Exponential backoff
79
80 logger.error(f"Webhook delivery failed after {webhook['max_retries']} attempts")
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:
1class PerformanceManager:
2 def __init__(self):
3 self.performance_targets = {
4 "cpu_only": {"fps": 10, "latency_ms": 150},
5 "gpu_basic": {"fps": 20, "latency_ms": 100},
6 "gpu_high_end": {"fps": 30, "latency_ms": 75}
7 }
8
9 def detect_hardware_capability(self):
10 """Detect available hardware and set performance targets"""
11 if torch.cuda.is_available():
12 gpu_memory = torch.cuda.get_device_properties(0).total_memory
13 if gpu_memory > 8e9: # 8GB+
14 return "gpu_high_end"
15 else:
16 return "gpu_basic"
17 return "cpu_only"
18
19 def optimize_for_hardware(self, capability):
20 """Optimize processing pipeline for detected hardware"""
21 targets = self.performance_targets[capability]
22
23 # Adjust batch sizes
24 if capability == "gpu_high_end":
25 self.batch_size = 8
26 self.model_precision = torch.float16
27 elif capability == "gpu_basic":
28 self.batch_size = 4
29 self.model_precision = torch.float32
30 else:
31 self.batch_size = 1
32 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:
1class ResourceManager:
2 def __init__(self, max_memory_gb=4, max_gpu_memory_gb=2):
3 self.max_memory = max_memory_gb * 1e9
4 self.max_gpu_memory = max_gpu_memory_gb * 1e9
5 self.memory_monitor = MemoryMonitor()
6
7 async def monitor_resources(self):
8 """Continuously monitor system resources"""
9 while True:
10 memory_usage = self.memory_monitor.get_memory_usage()
11 gpu_usage = self.memory_monitor.get_gpu_memory_usage()
12
13 if memory_usage > 0.9 * self.max_memory:
14 await self.trigger_memory_cleanup()
15
16 if gpu_usage > 0.9 * self.max_gpu_memory:
17 await self.trigger_gpu_cleanup()
18
19 await asyncio.sleep(5) # Check every 5 seconds
20
21 async def trigger_memory_cleanup(self):
22 """Trigger memory cleanup procedures"""
23 # Clear data buffers
24 self.data_buffer.clear_old_entries()
25 # Force garbage collection
26 gc.collect()
27 # Reduce batch sizes temporarily
28 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
1class ModelOptimizer:
2 def __init__(self, model):
3 self.model = model
4
5 def apply_quantization(self, quantization_type="dynamic"):
6 """Apply quantization to reduce model size"""
7 if quantization_type == "dynamic":
8 # Dynamic quantization for CPU inference
9 quantized_model = torch.quantization.quantize_dynamic(
10 self.model,
11 {torch.nn.Linear, torch.nn.Conv2d},
12 dtype=torch.qint8
13 )
14 elif quantization_type == "static":
15 # Static quantization for better performance
16 quantized_model = self.apply_static_quantization()
17
18 return quantized_model
19
20 def apply_pruning(self, sparsity=0.3):
21 """Apply structured pruning to reduce model complexity"""
22 import torch.nn.utils.prune as prune
23
24 for module in self.model.modules():
25 if isinstance(module, torch.nn.Conv2d):
26 prune.l1_unstructured(module, name='weight', amount=sparsity)
27
28 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
1class InferenceBatcher:
2 def __init__(self, max_batch_size=8, max_wait_time=0.01):
3 self.max_batch_size = max_batch_size
4 self.max_wait_time = max_wait_time
5 self.pending_requests = []
6 self.batch_timer = None
7
8 async def add_request(self, csi_data, callback):
9 """Add inference request to batch"""
10 request = {
11 'data': csi_data,
12 'callback': callback,
13 'timestamp': time.time()
14 }
15
16 self.pending_requests.append(request)
17
18 if len(self.pending_requests) >= self.max_batch_size:
19 await self.process_batch()
20 elif self.batch_timer is None:
21 self.batch_timer = asyncio.create_task(
22 self.wait_and_process()
23 )
24
25 async def process_batch(self):
26 """Process current batch of requests"""
27 if not self.pending_requests:
28 return
29
30 # Extract data and callbacks
31 batch_data = [req['data'] for req in self.pending_requests]
32 callbacks = [req['callback'] for req in self.pending_requests]
33
34 # Process batch
35 batch_tensor = torch.stack(batch_data)
36 with torch.no_grad():
37 batch_results = self.model(batch_tensor)
38
39 # Return results to callbacks
40 for i, callback in enumerate(callbacks):
41 await callback(batch_results[i])
42
43 # Clear processed requests
44 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
1class OptimizedCSIProcessor:
2 def __init__(self, num_workers=4):
3 self.num_workers = num_workers
4 self.processing_pool = ProcessPoolExecutor(max_workers=num_workers)
5 self.data_queue = asyncio.Queue(maxsize=1000)
6
7 async def start_processing(self):
8 """Start parallel CSI processing workers"""
9 tasks = []
10 for i in range(self.num_workers):
11 task = asyncio.create_task(self.processing_worker(i))
12 tasks.append(task)
13
14 await asyncio.gather(*tasks)
15
16 def process_csi_data(self, csi_data):
17 """CPU-intensive CSI processing in separate process"""
18 # Phase unwrapping
19 phase_unwrapped = np.unwrap(np.angle(csi_data), axis=-1)
20
21 # Amplitude processing
22 amplitude_db = 20 * np.log10(np.abs(csi_data) + 1e-10)
23
24 # Apply filters using optimized NumPy operations
25 filtered_phase = scipy.signal.savgol_filter(phase_unwrapped, 5, 2, axis=-1)
26 filtered_amplitude = scipy.signal.savgol_filter(amplitude_db, 5, 2, axis=-1)
27
28 # Combine and normalize
29 processed = np.stack([filtered_amplitude, filtered_phase], axis=-1)
30 normalized = (processed - processed.mean()) / (processed.std() + 1e-10)
31
32 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
1# Build stage
2FROM python:3.9-slim as builder
3
4WORKDIR /app
5COPY requirements.txt .
6RUN pip install --no-cache-dir --user -r requirements.txt
7
8# Production stage
9FROM python:3.9-slim
10
11# Install system dependencies
12RUN apt-get update && apt-get install -y \
13 libgl1-mesa-glx \
14 libglib2.0-0 \
15 libsm6 \
16 libxext6 \
17 libxrender-dev \
18 libgomp1 \
19 && rm -rf /var/lib/apt/lists/*
20
21# Copy Python packages from builder
22COPY --from=builder /root/.local /root/.local
23ENV PATH=/root/.local/bin:$PATH
24
25# Copy application code
26WORKDIR /app
27COPY . .
28
29# Create non-root user
30RUN useradd -m -u 1000 wifipose && \
31 chown -R wifipose:wifipose /app
32USER wifipose
33
34# Health check
35HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
36 CMD curl -f http://localhost:8000/health || exit 1
37
38EXPOSE 8000
39CMD ["python", "-m", "wifi_densepose.main"]
7.1.2 Kubernetes Deployment
Production Deployment Configuration:
1apiVersion: apps/v1
2kind: Deployment
3metadata:
4 name: wifi-densepose
5 labels:
6 app: wifi-densepose
7spec:
8 replicas: 3
9 selector:
10 matchLabels:
11 app: wifi-densepose
12 template:
13 metadata:
14 labels:
15 app: wifi-densepose
16 spec:
17 containers:
18 - name: wifi-densepose
19 image: wifi-densepose:latest
20 ports:
21 - containerPort: 8000
22 env:
23 - name: CUDA_VISIBLE_DEVICES
24 value: "0"
25 - name: LOG_LEVEL
26 value: "INFO"
27 resources:
28 requests:
29 memory: "2Gi"
30 cpu: "1000m"
31 nvidia.com/gpu: 1
32 limits:
33 memory: "4Gi"
34 cpu: "2000m"
35 nvidia.com/gpu: 1
36 livenessProbe:
37 httpGet:
38 path: /health
39 port: 8000
40 initialDelaySeconds: 60
41 periodSeconds: 30
42 readinessProbe:
43 httpGet:
44 path: /ready
45 port: 8000
46 initialDelaySeconds: 30
47 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
1from prometheus_client import Counter, Histogram, Gauge, start_http_server
2
3class MetricsCollector:
4 def __init__(self):
5 # Performance metrics
6 self.inference_duration = Histogram(
7 'inference_duration_seconds',
8 'Time spent on neural network inference',
9 buckets=[0.01, 0.025, 0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1.0]
10 )
11
12 self.pose_detection_count = Counter(
13 'pose_detections_total',
14 'Total number of pose detections',
15 ['confidence_level']
16 )
17
18 self.active_persons = Gauge(
19 'active_persons_current',
20 'Current number of tracked persons'
21 )
22
23 # System metrics
24 self.memory_usage = Gauge(
25 'memory_usage_bytes',
26 'Current memory usage in bytes'
27 )
28
29 self.gpu_utilization = Gauge(
30 'gpu_utilization_percent',
31 'GPU utilization percentage'
32 )
33
34 def record_inference_time(self, duration):
35 """Record neural network inference time"""
36 self.inference_duration.observe(duration)
37
38 def start_metrics_server(self, port=8001):
39 """Start Prometheus metrics server"""
40 start_http_server(port)
41 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
1class PrivacyPreservingProcessor:
2 def __init__(self):
3 self.data_retention_days = 7 # Configurable retention period
4 self.anonymization_enabled = True
5
6 def process_pose_data(self, raw_poses):
7 """Process poses with privacy preservation"""
8 if self.anonymization_enabled:
9 # Remove personally identifiable features
10 anonymized_poses = self.anonymize_poses(raw_poses)
11 return anonymized_poses
12 return raw_poses
13
14 def anonymize_poses(self, poses):
15 """Remove identifying characteristics from pose data"""
16 anonymized = []
17
18 for pose in poses:
19 # Remove fine-grained features that could identify individuals
20 anonymized_pose = {
21 'keypoints': self.generalize_keypoints(pose['keypoints']),
22 'confidence': pose['confidence'],
23 'timestamp': pose['timestamp'],
24 'activity': pose.get('activity', 'unknown')
25 }
26 anonymized.append(anonymized_pose)
27
28 return anonymized
29
30 async def cleanup_old_data(self):
31 """Automatically delete old data based on retention policy"""
32 cutoff_date = datetime.now() - timedelta(days=self.data_retention_days)
33
34 # Delete old pose data
35 await self.database.delete_poses_before(cutoff_date)
36
37 # Delete old CSI data
38 await self.database.delete_csi_before(cutoff_date)
39
40 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
1import pytest
2import asyncio
3from unittest.mock import Mock, AsyncMock, patch
4import numpy as np
5import torch
6
7class TestPoseEstimationPipeline:
8 """Test suite following London School TDD principles"""
9
10 @pytest.fixture
11 def mock_csi_data(self):
12 """Generate synthetic CSI data for testing"""
13 return np.random.complex128((3, 3, 56, 100)) # 3x3 MIMO, 56 subcarriers, 100 samples
14
15 @pytest.fixture
16 def mock_neural_network(self):
17 """Mock neural network for isolated testing"""
18 mock_network = Mock()
19 mock_network.forward.return_value = torch.randn(1, 17, 3) # Mock pose output
20 return mock_network
21
22 async def test_csi_preprocessing_pipeline(self, mock_csi_data):
23 """Test CSI preprocessing produces valid output"""
24 # Arrange
25 processor = CSIProcessor()
26
27 # Act
28 processed_data = await processor.preprocess(mock_csi_data)
29
30 # Assert
31 assert processed_data.shape == (3, 3, 56, 100)
32 assert not np.isnan(processed_data).any()
33 assert not np.isinf(processed_data).any()
34
35 # Verify phase unwrapping
36 phase_data = np.angle(processed_data)
37 phase_diff = np.diff(phase_data, axis=-1)
38 assert np.abs(phase_diff).max() < np.pi # No phase jumps > π
39
40 async def test_neural_network_inference_performance(self, mock_csi_data, mock_neural_network):
41 """Test neural network inference meets performance requirements"""
42 # Arrange
43 estimator = PoseEstimator()
44 estimator.neural_network = mock_neural_network
45
46 # Act
47 start_time = time.time()
48 result = await estimator.neural_inference(mock_csi_data)
49 inference_time = time.time() - start_time
50
51 # Assert
52 assert inference_time < 0.05 # <50ms requirement
53 assert result is not None
54 mock_neural_network.forward.assert_called_once()
55
56 async def test_fall_detection_accuracy(self):
57 """Test fall detection algorithm accuracy"""
58 # Arrange
59 fall_detector = FallDetector()
60
61 # Simulate fall trajectory
62 fall_trajectory = [
63 {'position': np.array([100, 100]), 'timestamp': 0.0}, # Standing
64 {'position': np.array([100, 120]), 'timestamp': 0.5}, # Falling
65 {'position': np.array([100, 180]), 'timestamp': 1.0}, # On ground
66 {'position': np.array([100, 180]), 'timestamp': 1.5}, # Still on ground
67 ]
68
69 # Act
70 fall_detected = False
71 for pose in fall_trajectory:
72 result = fall_detector.analyze_pose(pose)
73 if result['fall_detected']:
74 fall_detected = True
75 break
76
77 # Assert
78 assert fall_detected
79 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
