v1/docs/implementation-plan.md
This document outlines a comprehensive plan to fully implement WiFi-based pose detection functionality in the WiFi-DensePose system. Based on the system review, while the architecture and infrastructure are professionally implemented, the core WiFi CSI processing and machine learning components require complete implementation.
The implementation will follow a 4-phase approach to minimize risk and ensure systematic progress:
Based on 2024 research, the following hardware platforms support CSI extraction:
Objective: Establish reliable CSI data collection from WiFi hardware
Tasks:
Hardware Procurement and Setup
CSI Data Collection Implementation
src/hardware/csi_extractor.py:
Router Interface Development
src/hardware/router_interface.py:
src/core/router_interface.py:
Deliverables:
Objective: Implement basic CSI preprocessing and validation
Tasks:
CSI Data Preprocessing
src/core/phase_sanitizer.py:
Signal Quality Assessment
Data Validation Pipeline
Deliverables:
Objective: Develop sophisticated CSI processing for human detection
Tasks:
Human Detection Algorithms
src/core/csi_processor.py:
Feature Extraction
Environmental Calibration
Deliverables:
Objective: Integrate signal processing with existing system architecture
Tasks:
Service Integration
src/services/pose_service.py:
Streaming Pipeline
Performance Optimization
Deliverables:
Objective: Develop training pipeline for WiFi-to-pose domain adaptation
Tasks:
Data Collection and Annotation
Domain Adaptation Framework
src/models/modality_translation.py:
Training Pipeline
Deliverables:
Objective: Integrate trained models with inference pipeline
Tasks:
Model Loading and Inference
src/models/densepose_head.py:
Pose Estimation Pipeline
Output Processing
Deliverables:
Objective: Optimize models for production deployment
Tasks:
Model Optimization
Validation and Testing
Performance Benchmarking
Deliverables:
Objective: Complete end-to-end system integration
Tasks:
Full System Integration
API Completion
Database Integration
Deliverables:
Objective: Prepare system for production deployment
Tasks:
Production Optimization
Documentation and Training
Performance Monitoring
Deliverables:
# CSI Processing and Signal Analysis
"scapy>=2.5.0", # Packet capture and analysis
"pyserial>=3.5", # Serial communication with ESP32
"paho-mqtt>=1.6.0", # MQTT for ESP32 communication
# Advanced Signal Processing
"librosa>=0.10.0", # Audio/signal processing algorithms
"scipy.fftpack>=1.11.0", # FFT operations
"statsmodels>=0.14.0", # Statistical analysis
# Computer Vision and DensePose
"detectron2>=0.6", # Facebook's DensePose implementation
"fvcore>=0.1.5", # Required for Detectron2
"iopath>=0.1.9", # I/O operations for models
# Model Training and Optimization
"wandb>=0.15.0", # Experiment tracking
"tensorboard>=2.13.0", # Training visualization
"pytorch-lightning>=2.0", # Training framework
"torchmetrics>=1.0.0", # Model evaluation metrics
# Hardware Integration
"pyftdi>=0.54.0", # USB-to-serial communication
"hidapi>=0.13.0", # HID device communication
Risk: Inconsistent or noisy CSI data affecting model performance Mitigation:
Risk: Difficulty in translating CSI features to visual domain Mitigation:
Risk: System unable to meet real-time latency requirements Mitigation:
Risk: CSI-capable hardware may be limited or inconsistent Mitigation:
Risk: Domain adaptation models may not converge effectively Solution: Implement multiple training strategies and model architectures
Risk: Challenges in detecting multiple people simultaneously Solution: Start with single-person detection, gradually expand capability
Risk: Other WiFi devices and RF interference affecting performance Solution: Implement adaptive filtering and interference rejection
| Phase | Duration | Key Deliverables |
|---|---|---|
| Phase 1: Hardware Foundation | 4-6 weeks | CSI data collection, router interface, signal preprocessing |
| Phase 2: Signal Processing | 6-8 weeks | Human detection algorithms, real-time processing pipeline |
| Phase 3: ML Integration | 8-12 weeks | Domain adaptation, DensePose models, pose estimation |
| Phase 4: Production | 4-6 weeks | System integration, optimization, deployment |
| Total Project Duration | 22-32 weeks | Fully functional WiFi-based pose detection system |
This implementation plan provides a structured approach to building a fully functional WiFi-based pose detection system. The phase-based approach minimizes risk while ensuring systematic progress toward the goal. The existing architecture provides an excellent foundation, requiring focused effort on CSI processing, machine learning integration, and hardware interfaces.
Success depends on:
The plan balances technical ambition with practical constraints, providing clear milestones and deliverables for each phase of development.