- ToC: Add ruvector GitHub link and integration point count
- RVF Container: Add deployment targets table (ESP32 0.7MB to server
50MB), link to rvf crate family on GitHub
- Training: Add RuVector column to pipeline table showing which crate
powers each phase, add SONA component breakdown table, link arXiv
- RuVector Crates: Split into 5 directly-used (with integration
points mapped to exact .rs files) and 6 additional vendored, add
crates.io and GitHub source links for all 11
Co-Authored-By: claude-flow <ruv@ruv.net>
Add collapsible Use Cases & Applications section organized from
practical (elderly care, hospitals, retail) to specialized (events,
warehouses) to extreme (search & rescue, through-wall). Includes
hardware requirements and scaling notes per category.
Fix multi-person description to reflect reality: no hard software
limit, practical ceiling is signal physics (~3-5 per AP at 56
subcarriers, linear scaling with multi-AP).
Co-Authored-By: claude-flow <ruv@ruv.net>
The 10-person limit is just the default setting (pose_max_persons=10).
The API accepts 1-50, docs show configs up to 50, and Rust uses Option<u8>.
Co-Authored-By: claude-flow <ruv@ruv.net>
Promotes Installation and Quick Start to top-level sections placed
between Key Features and Table of Contents for faster onboarding.
Co-Authored-By: claude-flow <ruv@ruv.net>
Consumer WiFi does not expose Channel State Information — clarify that
pose estimation, vital signs, and through-wall sensing require ESP32-S3
or a research NIC. Added Full CSI column to hardware options table.
Co-Authored-By: claude-flow <ruv@ruv.net>
- Add docker/ folder with Dockerfile.rust (132MB), Dockerfile.python (569MB),
and docker-compose.yml
- Remove stale root-level Dockerfile and docker-compose files
- Implement --export-rvf CLI flag for standalone RVF package generation
- Generate wifi-densepose-v1.rvf (13KB) with model weights, vital config,
SONA profile, and training provenance
- Update README with Docker pull/run commands and RVF export instructions
- Update test count to 542+ and fix Docker port mappings
- Reply to issues #43, #44, #45 with Docker/RVF availability
Co-Authored-By: claude-flow <ruv@ruv.net>
Update ESP32 section with download-flash-provision workflow that
requires no build toolchain. Links to release v0.1.0-esp32 and
tutorial issue #34.
Co-Authored-By: claude-flow <ruv@ruv.net>
- install.sh: 7-step interactive installer detecting system, toolchains,
WiFi hardware (interfaces, ESP32 USB, Intel CSI debug), and recommending
the best build profile (verify/python/rust/browser/iot/docker/field/full)
- Rust is the primary recommended runtime (810x faster than Python)
- Makefile: 15+ targets including make install, make check, make build-rust,
make build-wasm, make bench, make run-api, make run-viz
- README: Updated installation section with Rust-primary ordering, removed
mock testing references, added v2.2.0 changelog entry
https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
Add prominent hardware requirements table at top of README documenting
the three paths to real CSI data (ESP32, research NIC, commodity WiFi).
Include remaining Three.js visualization components for dashboard.
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
- 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.
