- core/router_interface.py: Replace placeholder _collect_real_csi_data()
with explicit RuntimeError directing users to hardware setup docs
- hardware/router_interface.py: Replace np.random.rand() in
_parse_csi_response() with RouterConnectionError requiring real parser
- testing/: New isolated module for mock data generation (moved out of
production code paths per ADR-011)
- sensing/: Initialize commodity sensing module (ADR-013)
No production code path returns random data. Mock mode requires explicit
opt-in via WIFI_DENSEPOSE_MOCK=true environment variable.
https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
- csi_processor.py: Replace np.random.rand(10) Doppler placeholder with
real temporal phase-difference FFT extraction from CSI history buffer.
Returns zeros (not random) when insufficient history frames available.
- csi_extractor.py: Replace np.random.rand() fallbacks in ESP32 and
Atheros parsers with proper data parsing (ESP32) and explicit error
raising (Atheros). Add CSIExtractionError for clear failure reporting
instead of silent random data substitution.
These are the two most critical mock eliminations identified in ADR-011.
https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
- Add detailed wifi-Mat user guide covering:
- Installation and setup
- Detection capabilities (breathing, heartbeat, movement)
- Localization system (triangulation, depth estimation)
- START protocol triage classification
- Alert system with priority escalation
- Field deployment guide
- Hardware setup requirements
- API reference and troubleshooting
- Update main README.md with wifi-Mat section and links
- Fix compilation issues:
- Add missing deadline field in AlertPayload
- Fix type ambiguity in powi calls
- Resolve borrow checker issues in scan_cycle
- Export CsiDataBuffer from detection module
- Add missing imports in test modules
- All 83 tests now passing
- Created comprehensive API reference documentation covering authentication, request/response formats, error handling, and various API endpoints for pose estimation, system management, health checks, and WebSocket interactions.
- Developed a detailed deployment guide outlining prerequisites, Docker and Kubernetes deployment steps, cloud deployment options for AWS, GCP, and Azure, and configuration for production environments.
- Added HardwareService for managing router interfaces, data collection, and monitoring.
- Introduced PoseService for processing CSI data and estimating poses using neural networks.
- Created StreamService for real-time data streaming via WebSocket connections.
- Implemented initialization, start, stop, and status retrieval methods for each service.
- Added data processing, error handling, and statistics tracking across services.
- Integrated mock data generation for development and testing purposes.
- Added CSIExtractor class for extracting CSI data from WiFi routers.
- Implemented RouterInterface class for SSH communication with routers.
- Developed DensePoseHead class for body part segmentation and UV coordinate regression.
- Created unit tests for CSIExtractor and RouterInterface to ensure functionality and error handling.
- Integrated paramiko for SSH connections and command execution.
- Established configuration validation for both extractor and router interface.
- Added context manager support for resource management in both classes.
- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads.
- Added a comprehensive training utility for the model, including loss functions and training steps.
- Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.
