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CHANGELOG.md

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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Unreleased

Added

  • Project MERIDIAN (ADR-027) — Cross-environment domain generalization for WiFi pose estimation (1,858 lines, 72 tests)
    • HardwareNormalizer — Catmull-Rom cubic interpolation resamples any hardware CSI to canonical 56 subcarriers; z-score + phase sanitization
    • DomainFactorizer + GradientReversalLayer — adversarial disentanglement of pose-relevant vs environment-specific features
    • GeometryEncoder + FilmLayer — Fourier positional encoding + DeepSets + FiLM for zero-shot deployment given AP positions
    • VirtualDomainAugmentor — synthetic environment diversity (room scale, wall material, scatterers, noise) for 4x training augmentation
    • RapidAdaptation — 10-second unsupervised calibration via contrastive test-time training + LoRA adapters
    • CrossDomainEvaluator — 6-metric evaluation protocol (MPJPE in-domain/cross-domain/few-shot/cross-hardware, domain gap ratio, adaptation speedup)
  • ADR-027: Cross-Environment Domain Generalization — 10 SOTA citations (PerceptAlign, X-Fi ICLR 2025, AM-FM, DGSense, CVPR 2024)
  • Cross-platform RSSI adapters — macOS CoreWLAN (MacosCoreWlanScanner) and Linux iw (LinuxIwScanner) Rust adapters with #[cfg(target_os)] gating
  • macOS CoreWLAN Python sensing adapter with Swift helper (mac_wifi.swift)
  • macOS synthetic BSSID generation (FNV-1a hash) for Sonoma 14.4+ BSSID redaction
  • Linux iw dev <iface> scan parser with freq-to-channel conversion and scan dump (no-root) mode
  • ADR-025: macOS CoreWLAN WiFi Sensing (ORCA)

Fixed

  • Removed synthetic byte counters from Python MacosWifiCollector — now reports tx_bytes=0, rx_bytes=0 instead of fake incrementing values

3.0.0 - 2026-03-01

Major release: AETHER contrastive embedding model, Docker Hub images, and comprehensive UI overhaul.

Added — AETHER Contrastive Embedding Model (ADR-024)

  • Project AETHER — self-supervised contrastive learning for WiFi CSI fingerprinting, similarity search, and anomaly detection (9bbe956)
  • embedding.rs module: ProjectionHead, InfoNceLoss, CsiAugmenter, FingerprintIndex, PoseEncoder, EmbeddingExtractor (909 lines, zero external ML dependencies)
  • SimCLR-style pretraining with 5 physically-motivated augmentations (temporal jitter, subcarrier masking, Gaussian noise, phase rotation, amplitude scaling)
  • CLI flags: --pretrain, --pretrain-epochs, --embed, --build-index <type>
  • Four HNSW-compatible fingerprint index types: env_fingerprint, activity_pattern, temporal_baseline, person_track
  • Cross-modal PoseEncoder for WiFi-to-camera embedding alignment
  • VICReg regularization for embedding collapse prevention
  • 53K total parameters (55 KB at INT8) — fits on ESP32

Added — Docker & Deployment

  • Published Docker Hub images: ruvnet/wifi-densepose:latest (132 MB Rust) and ruvnet/wifi-densepose:python (569 MB) (add9f19)
  • Multi-stage Dockerfile for Rust sensing server with RuVector crates
  • docker-compose.yml orchestrating both Rust and Python services
  • RVF model export via --export-rvf and load via --load-rvf CLI flags

Added — Documentation

  • 33 use cases across 4 vertical tiers: Everyday, Specialized, Robotics & Industrial, Extreme (0afd9c5)
  • "Why WiFi Wins" comparison table (WiFi vs camera vs LIDAR vs wearable vs PIR)
  • Mermaid architecture diagrams: end-to-end pipeline, signal processing detail, deployment topology (50f0fc9)
  • Models & Training section with RuVector crate links (GitHub + crates.io), SONA component table (965a1cc)
  • RVF container section with deployment targets table (ESP32 0.7 MB to server 50+ MB)
  • Collapsible README sections for improved navigation (478d964, 99ec980, 0ebd6be)
  • Installation and Quick Start moved above Table of Contents (50acbf7)
  • CSI hardware requirement notice (528b394)

Fixed

  • UI auto-detects server port from page origin — no more hardcoded localhost:8080; works on any port (Docker :3000, native :8080, custom) (3b72f35, closes #55)
  • Docker port mismatch — server now binds 3000/3001 inside container as documented (44b9c30)
  • Added /ws/sensing WebSocket route to the HTTP server so UI only needs one port
  • Fixed README API endpoint references: /api/v1/health → /health, /api/v1/sensing → /api/v1/sensing/latest
  • Multi-person tracking limit corrected: configurable default 10, no hard software cap (e2ce250)

2.0.0 - 2026-02-28

Major release: complete Rust sensing server, full DensePose training pipeline, RuVector v2.0.4 integration, ESP32-S3 firmware, and 6 security hardening patches.

Added — Rust Sensing Server

  • Full DensePose-compatible REST API served by Axum (d956c30)
    • GET /health — server health
    • GET /api/v1/sensing/latest — live CSI sensing data
    • GET /api/v1/vital-signs — breathing rate (6-30 BPM) and heartbeat (40-120 BPM)
    • GET /api/v1/pose/current — 17 COCO keypoints derived from WiFi signal field
    • GET /api/v1/info — server build and feature info
    • GET /api/v1/model/info — RVF model container metadata
    • ws://host/ws/sensing — real-time WebSocket stream
  • Three data sources: --source esp32 (UDP CSI), --source windows (netsh RSSI), --source simulated (deterministic reference)
  • Auto-detection: server probes ESP32 UDP and Windows WiFi, falls back to simulated
  • Three.js visualization UI with 3D body skeleton, signal heatmap, phase plot, Doppler bars, vital signs panel
  • Static UI serving via --ui-path flag
  • Throughput: 9,520–11,665 frames/sec (release build)

Added — ADR-021: Vital Sign Detection

  • VitalSignDetector with breathing (6-30 BPM) and heartbeat (40-120 BPM) extraction from CSI fluctuations (1192de9)
  • FFT-based spectral analysis with configurable band-pass filters
  • Confidence scoring based on spectral peak prominence
  • REST endpoint /api/v1/vital-signs with real-time JSON output

Added — ADR-023: DensePose Training Pipeline (Phases 1-8)

  • wifi-densepose-train crate with complete 8-phase pipeline (fc409df, ec98e40, fce1271)
    • Phase 1: DataPipeline with MM-Fi and Wi-Pose dataset loaders
    • Phase 2: CsiToPoseTransformer — 4-head cross-attention + 2-layer GCN on COCO skeleton
    • Phase 3: 6-term composite loss (MSE, bone length, symmetry, joint angle, temporal, confidence)
    • Phase 4: DynamicPersonMatcher via ruvector-mincut (O(n^1.5 log n) Hungarian assignment)
    • Phase 5: SonaAdapter — MicroLoRA rank-4 with EWC++ memory preservation
    • Phase 6: SparseInference — progressive 3-layer model loading (A: essential, B: refinement, C: full)
    • Phase 7: RvfContainer — single-file model packaging with segment-based binary format
    • Phase 8: End-to-end training with cosine-annealing LR, early stopping, checkpoint saving
  • CLI: --train, --dataset, --epochs, --save-rvf, --load-rvf, --export-rvf
  • Benchmark: ~11,665 fps inference, 229 tests passing

Added — ADR-016: RuVector Training Integration (all 5 crates)

  • ruvector-mincut → DynamicPersonMatcher in metrics.rs + subcarrier selection (81ad09d, a7dd31c)
  • ruvector-attn-mincut → antenna attention in model.rs + noise-gated spectrogram
  • ruvector-temporal-tensor → CompressedCsiBuffer in dataset.rs + compressed breathing/heartbeat
  • ruvector-solver → sparse subcarrier interpolation (114→56) + Fresnel triangulation
  • ruvector-attention → spatial attention in model.rs + attention-weighted BVP
  • Vendored all 11 RuVector crates under vendor/ruvector/ (d803bfe)

Added — ADR-017: RuVector Signal & MAT Integration (7 integration points)

  • gate_spectrogram() — attention-gated noise suppression (18170d7)
  • attention_weighted_bvp() — sensitivity-weighted velocity profiles
  • mincut_subcarrier_partition() — dynamic sensitive/insensitive subcarrier split
  • solve_fresnel_geometry() — TX-body-RX distance estimation
  • CompressedBreathingBuffer + CompressedHeartbeatSpectrogram
  • BreathingDetector + HeartbeatDetector (MAT crate, real FFT + micro-Doppler)
  • Feature-gated behind cfg(feature = "ruvector") (ab2453e)

Added — ADR-018: ESP32-S3 Firmware & Live CSI Pipeline

  • ESP32-S3 firmware with FreeRTOS CSI extraction (92a5182)
  • ADR-018 binary frame format: [0xAD, 0x18, len_hi, len_lo, payload]
  • Rust Esp32Aggregator receiving UDP frames on port 5005
  • bridge.rs converting I/Q pairs to amplitude/phase vectors
  • NVS provisioning for WiFi credentials
  • Pre-built binary quick start documentation (696a726)

Added — ADR-014: SOTA Signal Processing

  • 6 algorithms, 83 tests (fcb93cc)
    • Hampel filter (median + MAD, resistant to 50% contamination)
    • Conjugate multiplication (reference-antenna ratio, cancels common-mode noise)
    • Phase sanitization (unwrap + linear detrend, removes CFO/SFO)
    • Fresnel zone geometry (TX-body-RX distance from first-principles physics)
    • Body Velocity Profile (micro-Doppler extraction, 5.7x speedup)
    • Attention-gated spectrogram (learned noise suppression)

Added — ADR-015: Public Dataset Training Strategy

  • MM-Fi and Wi-Pose dataset specifications with download links (4babb32, 5dc2f66)
  • Verified dataset dimensions, sampling rates, and annotation formats
  • Cross-dataset evaluation protocol

Added — WiFi-Mat Disaster Detection Module

  • Multi-AP triangulation for through-wall survivor detection (a17b630, 6b20ff0)
  • Triage classification (breathing, heartbeat, motion)
  • Domain events: survivor_detected, survivor_updated, alert_created
  • WebSocket broadcast at /ws/mat/stream

Added — Infrastructure

  • Guided 7-step interactive installer with 8 hardware profiles (8583f3e)
  • Comprehensive build guide for Linux, macOS, Windows, Docker, ESP32 (45f8a0d)
  • 12 Architecture Decision Records (ADR-001 through ADR-012) (337dd96)

Added — UI & Visualization

  • Sensing-only UI mode with Gaussian splat visualization (b7e0f07)
  • Three.js 3D body model (17 joints, 16 limbs) with signal-viz components
  • Tabs: Dashboard, Hardware, Live Demo, Sensing, Architecture, Performance, Applications
  • WebSocket client with automatic reconnection and exponential backoff

Added — Rust Signal Processing Crate

  • Complete Rust port of WiFi-DensePose with modular workspace (6ed69a3)
    • wifi-densepose-signal — CSI processing, phase sanitization, feature extraction
    • wifi-densepose-core — shared types and configuration
    • wifi-densepose-nn — neural network inference (DensePose head, RCNN)
    • wifi-densepose-hardware — ESP32 aggregator, hardware interfaces
    • wifi-densepose-config — configuration management
  • Comprehensive benchmarks and validation tests (3ccb301)

Added — Python Sensing Pipeline

  • WindowsWifiCollector — RSSI collection via netsh wlan show networks
  • RssiFeatureExtractor — variance, spectral bands (motion 0.5-4 Hz, breathing 0.1-0.5 Hz), change points
  • PresenceClassifier — rule-based 3-state classification (ABSENT / PRESENT_STILL / ACTIVE)
  • Cross-receiver agreement scoring for multi-AP confidence boosting
  • WebSocket sensing server (ws_server.py) broadcasting JSON at 2 Hz
  • Deterministic CSI proof bundles for reproducible verification (v1/data/proof/)
  • Commodity sensing unit tests (b391638)

Changed

  • Rust hardware adapters now return explicit errors instead of silent empty data (6e0e539)

Fixed

  • Review fixes for end-to-end training pipeline (45f0304)
  • Dockerfile paths updated from src/ to v1/src/ (7872987)
  • IoT profile installer instructions updated for aggregator CLI (f460097)
  • process.env reference removed from browser ES module (e320bc9)

Performance

  • 5.7x Doppler extraction speedup via optimized FFT windowing (32c75c8)
  • Single 2.1 MB static binary, zero Python dependencies for Rust server

Security

  • Fix SQL injection in status command and migrations (f9d125d)
  • Fix XSS vulnerabilities in UI components (5db55fd)
  • Fix command injection in statusline.cjs (4cb01fd)
  • Fix path traversal vulnerabilities (896c4fc)
  • Fix insecure WebSocket connections — enforce wss:// on non-localhost (ac094d4)
  • Fix GitHub Actions shell injection (ab2e7b4)
  • Fix 10 additional vulnerabilities, remove 12 dead code instances (7afdad0)

1.1.0 - 2025-06-07

Added

  • Complete Python WiFi-DensePose system with CSI data extraction and router interface
  • CSI processing and phase sanitization modules
  • Batch processing for CSI data in CSIProcessor and PhaseSanitizer
  • Hardware, pose, and stream services for WiFi-DensePose API
  • Comprehensive CSS styles for UI components and dark mode support
  • API and Deployment documentation

Fixed

  • Badge links for PyPI and Docker in README
  • Async engine creation poolclass specification

1.0.0 - 2024-12-01

Added

  • Initial release of WiFi-DensePose
  • Real-time WiFi-based human pose estimation using Channel State Information (CSI)
  • DensePose neural network integration for body surface mapping
  • RESTful API with comprehensive endpoint coverage
  • WebSocket streaming for real-time pose data
  • Multi-person tracking with configurable capacity (default 10, up to 50+)
  • Fall detection and activity recognition
  • Domain configurations: healthcare, fitness, smart home, security
  • CLI interface for server management and configuration
  • Hardware abstraction layer for multiple WiFi chipsets
  • Phase sanitization and signal processing pipeline
  • Authentication and rate limiting
  • Background task management
  • Cross-platform support (Linux, macOS, Windows)

Documentation

  • User guide and API reference
  • Deployment and troubleshooting guides
  • Hardware setup and calibration instructions
  • Performance benchmarks
  • Contributing guidelines