diff --git a/README.md b/README.md
index c9f4a15..f230bba 100644
--- a/README.md
+++ b/README.md
@@ -1,1303 +1,120 @@
-# WiFi DensePose
+# WiFi DensePose — SCAM ALERT
-**See through walls with WiFi.** No cameras. No wearables. Just radio waves.
-
-WiFi DensePose turns commodity WiFi signals into real-time human pose estimation, vital sign monitoring, and presence detection — all without a single pixel of video. By analyzing Channel State Information (CSI) disturbances caused by human movement, the system reconstructs body position, breathing rate, and heartbeat using physics-based signal processing and machine learning.
-
-[](https://www.rust-lang.org/)
-[](https://opensource.org/licenses/MIT)
-[](https://github.com/ruvnet/wifi-densepose)
-[](https://hub.docker.com/r/ruvnet/wifi-densepose)
-[](#vital-sign-detection)
-[](#esp32-s3-hardware-pipeline)
-
-> | What | How | Speed |
-> |------|-----|-------|
-> | **Pose estimation** | CSI subcarrier amplitude/phase → DensePose UV maps | 54K fps (Rust) |
-> | **Breathing detection** | Bandpass 0.1-0.5 Hz → FFT peak | 6-30 BPM |
-> | **Heart rate** | Bandpass 0.8-2.0 Hz → FFT peak | 40-120 BPM |
-> | **Presence sensing** | RSSI variance + motion band power | < 1ms latency |
-> | **Through-wall** | Fresnel zone geometry + multipath modeling | Up to 5m depth |
-
-```bash
-# 30 seconds to live sensing — no toolchain required
-docker pull ruvnet/wifi-densepose:latest
-docker run -p 3000:3000 ruvnet/wifi-densepose:latest
-# Open http://localhost:3000
-```
-
-> [!NOTE]
-> **CSI-capable hardware required.** Pose estimation, vital signs, and through-wall sensing rely on Channel State Information (CSI) — per-subcarrier amplitude and phase data that standard consumer WiFi does not expose. You need CSI-capable hardware (ESP32-S3 or a research NIC) for full functionality. Consumer WiFi laptops can only provide RSSI-based presence detection, which is significantly less capable.
-
-> **Hardware options** for live CSI capture:
->
-> | Option | Hardware | Cost | Full CSI | Capabilities |
-> |--------|----------|------|----------|-------------|
-> | **ESP32 Mesh** (recommended) | 3-6x ESP32-S3 + WiFi router | ~$54 | Yes | Pose, breathing, heartbeat, motion, presence |
-> | **Research NIC** | Intel 5300 / Atheros AR9580 | ~$50-100 | Yes | Full CSI with 3x3 MIMO |
-> | **Any WiFi** | Windows/Linux laptop | $0 | No | RSSI-only: coarse presence and motion |
->
-> No hardware? Verify the signal processing pipeline with the deterministic reference signal: `python v1/data/proof/verify.py`
+> **This is a fork of [ruvnet/wifi-densepose](https://github.com/ruvnet/wifi-densepose) created solely to document the technical audit findings. The original project is a non-functional AI-generated facade with suspected star inflation.**
---
-## 📖 Documentation
+## Technical Audit: Core Implementation is Non-Functional
-| Document | Description |
-|----------|-------------|
-| [User Guide](docs/user-guide.md) | Step-by-step guide: installation, first run, API usage, hardware setup, training |
-| [WiFi-Mat User Guide](docs/wifi-mat-user-guide.md) | Disaster response module: search & rescue, START triage |
-| [Build Guide](docs/build-guide.md) | Building from source (Rust and Python) |
-| [Architecture Decisions](docs/adr/) | 24 ADRs covering signal processing, training, hardware, security |
+After a thorough independent code audit with cross-verification from three AI systems (Claude, Codex/GPT-5.2, Gemini), we confirm that **this project is a non-functional facade**. The core signal processing pipeline returns random/hardcoded data, neural network models have no trained weights, and the claimed performance metrics are fabricated.
+
+> **Note:** This was originally submitted as [Issue #29](https://github.com/ruvnet/wifi-densepose/issues/29) on the original repo but was **deleted by the maintainer** — which is why this fork exists.
---
-## 🚀 Key Features
+## Evidence
-| | Feature | What It Means |
-|---|---------|---------------|
-| 🔒 | **Privacy-First** | Tracks human pose using only WiFi signals — no cameras, no video, no images stored |
-| ⚡ | **Real-Time** | Analyzes WiFi signals in under 100 microseconds per frame — fast enough for live monitoring |
-| 💓 | **Vital Signs** | Detects breathing rate (6-30 breaths/min) and heart rate (40-120 bpm) without any wearable |
-| 👥 | **Multi-Person** | Tracks multiple people simultaneously, each with independent pose and vitals — no hard software limit (physics: ~3-5 per AP with 56 subcarriers, more with multi-AP) |
-| 🧱 | **Through-Wall** | WiFi passes through walls, furniture, and debris — works where cameras cannot |
-| 🚑 | **Disaster Response** | Detects trapped survivors through rubble and classifies injury severity (START triage) |
-| 🐳 | **One-Command Setup** | `docker pull ruvnet/wifi-densepose:latest` — live sensing in 30 seconds, no toolchain needed |
-| 📦 | **Portable Models** | Trained models package into a single `.rvf` file — runs on edge, cloud, or browser (WASM) |
-| 🦀 | **810x Faster** | Complete Rust rewrite: 54,000 frames/sec pipeline, 132 MB Docker image, 542+ tests |
-
----
-
-## 🏢 Use Cases & Applications
-
-WiFi sensing works anywhere WiFi exists. No new hardware in most cases — just software on existing access points or a $8 ESP32 add-on. Because there are no cameras, deployments avoid privacy regulations (GDPR video, HIPAA imaging) by design.
-
-**Scaling:** Each AP distinguishes ~3-5 people (56 subcarriers). Multi-AP multiplies linearly — a 4-AP retail mesh covers ~15-20 occupants. No hard software limit; the practical ceiling is signal physics.
-
-| | Why WiFi sensing wins | Traditional alternative |
-|---|----------------------|----------------------|
-| 🔒 | **No video, no GDPR/HIPAA imaging rules** | Cameras require consent, signage, data retention policies |
-| 🧱 | **Works through walls, shelving, debris** | Cameras need line-of-sight per room |
-| 🌙 | **Works in total darkness** | Cameras need IR or visible light |
-| 💰 | **$0-$8 per zone** (existing WiFi or ESP32) | Camera systems: $200-$2,000 per zone |
-| 🔌 | **WiFi already deployed everywhere** | PIR/radar sensors require new wiring per room |
-
-
-🏥 Everyday — Healthcare, retail, office, hospitality (commodity WiFi)
-
-| Use Case | What It Does | Hardware | Key Metric |
-|----------|-------------|----------|------------|
-| **Elderly care / assisted living** | Fall detection, nighttime activity monitoring, breathing rate during sleep — no wearable compliance needed | 1 ESP32-S3 per room ($8) | Fall alert <2s |
-| **Hospital patient monitoring** | Continuous breathing + heart rate for non-critical beds without wired sensors; nurse alert on anomaly | 1-2 APs per ward | Breathing: 6-30 BPM |
-| **Emergency room triage** | Automated occupancy count + wait-time estimation; detect patient distress (abnormal breathing) in waiting areas | Existing hospital WiFi | Occupancy accuracy >95% |
-| **Retail occupancy & flow** | Real-time foot traffic, dwell time by zone, queue length — no cameras, no opt-in, GDPR-friendly | Existing store WiFi + 1 ESP32 | Dwell resolution ~1m |
-| **Office space utilization** | Which desks/rooms are actually occupied, meeting room no-shows, HVAC optimization based on real presence | Existing enterprise WiFi | Presence latency <1s |
-| **Hotel & hospitality** | Room occupancy without door sensors, minibar/bathroom usage patterns, energy savings on empty rooms | Existing hotel WiFi | 15-30% HVAC savings |
-| **Restaurants & food service** | Table turnover tracking, kitchen staff presence, restroom occupancy displays — no cameras in dining areas | Existing WiFi | Queue wait ±30s |
-| **Parking garages** | Pedestrian presence in stairwells and elevators where cameras have blind spots; security alert if someone lingers | Existing WiFi | Through-concrete walls |
-
-
-
-
-🏟️ Specialized — Events, fitness, education, civic (CSI-capable hardware)
-
-| Use Case | What It Does | Hardware | Key Metric |
-|----------|-------------|----------|------------|
-| **Smart home automation** | Room-level presence triggers (lights, HVAC, music) that work through walls — no dead zones, no motion-sensor timeouts | 2-3 ESP32-S3 nodes ($24) | Through-wall range ~5m |
-| **Fitness & sports** | Rep counting, posture correction, breathing cadence during exercise — no wearable, no camera in locker rooms | 3+ ESP32-S3 mesh | Pose: 17 keypoints |
-| **Childcare & schools** | Naptime breathing monitoring, playground headcount, restricted-area alerts — privacy-safe for minors | 2-4 ESP32-S3 per zone | Breathing: ±1 BPM |
-| **Event venues & concerts** | Crowd density mapping, crush-risk detection via breathing compression, emergency evacuation flow tracking | Multi-AP mesh (4-8 APs) | Density per m² |
-| **Stadiums & arenas** | Section-level occupancy for dynamic pricing, concession staffing, emergency egress flow modeling | Enterprise AP grid | 15-20 per AP mesh |
-| **Houses of worship** | Attendance counting without facial recognition — privacy-sensitive congregations, multi-room campus tracking | Existing WiFi | Zone-level accuracy |
-| **Warehouse & logistics** | Worker safety zones, forklift proximity alerts, occupancy in hazardous areas — works through shelving and pallets | Industrial AP mesh | Alert latency <500ms |
-| **Civic infrastructure** | Public restroom occupancy (no cameras possible), subway platform crowding, shelter headcount during emergencies | Municipal WiFi + ESP32 | Real-time headcount |
-| **Museums & galleries** | Visitor flow heatmaps, exhibit dwell time, crowd bottleneck alerts — no cameras near artwork (flash/theft risk) | Existing WiFi | Zone dwell ±5s |
-
-
-
-
-🤖 Robotics & Industrial — Autonomous systems, manufacturing, android spatial awareness
-
-WiFi sensing gives robots and autonomous systems a spatial awareness layer that works where LIDAR and cameras fail — through dust, smoke, fog, and around corners. The CSI signal field acts as a "sixth sense" for detecting humans in the environment without requiring line-of-sight.
-
-| Use Case | What It Does | Hardware | Key Metric |
-|----------|-------------|----------|------------|
-| **Cobot safety zones** | Detect human presence near collaborative robots — auto-slow or stop before contact, even behind obstructions | 2-3 ESP32-S3 per cell | Presence latency <100ms |
-| **Warehouse AMR navigation** | Autonomous mobile robots sense humans around blind corners, through shelving racks — no LIDAR occlusion | ESP32 mesh along aisles | Through-shelf detection |
-| **Android / humanoid spatial awareness** | Ambient human pose sensing for social robots — detect gestures, approach direction, and personal space without cameras always on | Onboard ESP32-S3 module | 17-keypoint pose |
-| **Manufacturing line monitoring** | Worker presence at each station, ergonomic posture alerts, headcount for shift compliance — works through equipment | Industrial AP per zone | Pose + breathing |
-| **Construction site safety** | Exclusion zone enforcement around heavy machinery, fall detection from scaffolding, personnel headcount | Ruggedized ESP32 mesh | Alert <2s, through-dust |
-| **Agricultural robotics** | Detect farm workers near autonomous harvesters in dusty/foggy field conditions where cameras are unreliable | Weatherproof ESP32 nodes | Range ~10m open field |
-| **Drone landing zones** | Verify landing area is clear of humans — WiFi sensing works in rain, dust, and low light where downward cameras fail | Ground ESP32 nodes | Presence: >95% accuracy |
-| **Clean room monitoring** | Personnel tracking without cameras (particle contamination risk from camera fans) — gown compliance via pose | Existing cleanroom WiFi | No particulate emission |
-
-
-
-
-🔥 Extreme — Through-wall, disaster, defense, underground
-
-These scenarios exploit WiFi's ability to penetrate solid materials — concrete, rubble, earth — where no optical or infrared sensor can reach. The WiFi-Mat disaster module (ADR-001) is specifically designed for this tier.
-
-| Use Case | What It Does | Hardware | Key Metric |
-|----------|-------------|----------|------------|
-| **Search & rescue (WiFi-Mat)** | Detect survivors through rubble/debris via breathing signature, START triage color classification, 3D localization | Portable ESP32 mesh + laptop | Through 30cm concrete |
-| **Firefighting** | Locate occupants through smoke and walls before entry; breathing detection confirms life signs remotely | Portable mesh on truck | Works in zero visibility |
-| **Prison & secure facilities** | Cell occupancy verification, distress detection (abnormal vitals), perimeter sensing — no camera blind spots | Dedicated AP infrastructure | 24/7 vital signs |
-| **Military / tactical** | Through-wall personnel detection, room clearing confirmation, hostage vital signs at standoff distance | Directional WiFi + custom FW | Range: 5m through wall |
-| **Border & perimeter security** | Detect human presence in tunnels, behind fences, in vehicles — passive sensing, no active illumination to reveal position | Concealed ESP32 mesh | Passive / covert |
-| **Mining & underground** | Worker presence in tunnels where GPS/cameras fail, breathing detection after collapse, headcount at safety points | Ruggedized ESP32 mesh | Through rock/earth |
-| **Maritime & naval** | Below-deck personnel tracking through steel bulkheads (limited range, requires tuning), man-overboard detection | Ship WiFi + ESP32 | Through 1-2 bulkheads |
-| **Wildlife research** | Non-invasive animal activity monitoring in enclosures or dens — no light pollution, no visual disturbance | Weatherproof ESP32 nodes | Zero light emission |
-
-
-
----
-
-
-🧠 Self-Learning WiFi AI (ADR-024) — Adaptive recognition, self-optimization, and intelligent anomaly detection
-
-Every WiFi signal that passes through a room creates a unique fingerprint of that space. WiFi-DensePose already reads these fingerprints to track people, but until now it threw away the internal "understanding" after each reading. The Self-Learning WiFi AI captures and preserves that understanding as compact, reusable vectors — and continuously optimizes itself for each new environment.
-
-**What it does in plain terms:**
-- Turns any WiFi signal into a 128-number "fingerprint" that uniquely describes what's happening in a room
-- Learns entirely on its own from raw WiFi data — no cameras, no labeling, no human supervision needed
-- Recognizes rooms, detects intruders, identifies people, and classifies activities using only WiFi
-- Runs on an $8 ESP32 chip (the entire model fits in 60 KB of memory)
-- Produces both body pose tracking AND environment fingerprints in a single computation
-
-**Key Capabilities**
-
-| What | How it works | Why it matters |
-|------|-------------|----------------|
-| **Self-supervised learning** | The model watches WiFi signals and teaches itself what "similar" and "different" look like, without any human-labeled data | Deploy anywhere — just plug in a WiFi sensor and wait 10 minutes |
-| **Room identification** | Each room produces a distinct WiFi fingerprint pattern | Know which room someone is in without GPS or beacons |
-| **Anomaly detection** | An unexpected person or event creates a fingerprint that doesn't match anything seen before | Automatic intrusion and fall detection as a free byproduct |
-| **Person re-identification** | Each person disturbs WiFi in a slightly different way, creating a personal signature | Track individuals across sessions without cameras |
-| **Environment adaptation** | MicroLoRA adapters (1,792 parameters per room) fine-tune the model for each new space | Adapts to a new room with minimal data — 93% less than retraining from scratch |
-| **Memory preservation** | EWC++ regularization remembers what was learned during pretraining | Switching to a new task doesn't erase prior knowledge |
-| **Hard-negative mining** | Training focuses on the most confusing examples to learn faster | Better accuracy with the same amount of training data |
-
-**Architecture**
-
-```
-WiFi Signal [56 channels] → Transformer + Graph Neural Network
- ├→ 128-dim environment fingerprint (for search + identification)
- └→ 17-joint body pose (for human tracking)
-```
-
-**Quick Start**
-
-```bash
-# Step 1: Learn from raw WiFi data (no labels needed)
-cargo run -p wifi-densepose-sensing-server -- --pretrain --dataset data/csi/ --pretrain-epochs 50
-
-# Step 2: Fine-tune with pose labels for full capability
-cargo run -p wifi-densepose-sensing-server -- --train --dataset data/mmfi/ --epochs 100 --save-rvf model.rvf
-
-# Step 3: Use the model — extract fingerprints from live WiFi
-cargo run -p wifi-densepose-sensing-server -- --model model.rvf --embed
-
-# Step 4: Search — find similar environments or detect anomalies
-cargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index env
-```
-
-**Training Modes**
-
-| Mode | What you need | What you get |
-|------|--------------|-------------|
-| Self-Supervised | Just raw WiFi data | A model that understands WiFi signal structure |
-| Supervised | WiFi data + body pose labels | Full pose tracking + environment fingerprints |
-| Cross-Modal | WiFi data + camera footage | Fingerprints aligned with visual understanding |
-
-**Fingerprint Index Types**
-
-| Index | What it stores | Real-world use |
-|-------|---------------|----------------|
-| `env_fingerprint` | Average room fingerprint | "Is this the kitchen or the bedroom?" |
-| `activity_pattern` | Activity boundaries | "Is someone cooking, sleeping, or exercising?" |
-| `temporal_baseline` | Normal conditions | "Something unusual just happened in this room" |
-| `person_track` | Individual movement signatures | "Person A just entered the living room" |
-
-**Model Size**
-
-| Component | Parameters | Memory (on ESP32) |
-|-----------|-----------|-------------------|
-| Transformer backbone | ~28,000 | 28 KB |
-| Embedding projection head | ~25,000 | 25 KB |
-| Per-room MicroLoRA adapter | ~1,800 | 2 KB |
-| **Total** | **~55,000** | **55 KB** (of 520 KB available) |
-
-See [`docs/adr/ADR-024-contrastive-csi-embedding-model.md`](docs/adr/ADR-024-contrastive-csi-embedding-model.md) for full architectural details.
-
-
-
----
-
-## 📦 Installation
-
-
-Guided Installer — Interactive hardware detection and profile selection
-
-```bash
-./install.sh
-```
-
-The installer walks through 7 steps: system detection, toolchain check, WiFi hardware scan, profile recommendation, dependency install, build, and verification.
-
-| Profile | What it installs | Size | Requirements |
-|---------|-----------------|------|-------------|
-| `verify` | Pipeline verification only | ~5 MB | Python 3.8+ |
-| `python` | Full Python API server + sensing | ~500 MB | Python 3.8+ |
-| `rust` | Rust pipeline (~810x faster) | ~200 MB | Rust 1.70+ |
-| `browser` | WASM for in-browser execution | ~10 MB | Rust + wasm-pack |
-| `iot` | ESP32 sensor mesh + aggregator | varies | Rust + ESP-IDF |
-| `docker` | Docker-based deployment | ~1 GB | Docker |
-| `field` | WiFi-Mat disaster response kit | ~62 MB | Rust + wasm-pack |
-| `full` | Everything available | ~2 GB | All toolchains |
-
-```bash
-# Non-interactive
-./install.sh --profile rust --yes
-
-# Hardware check only
-./install.sh --check-only
-```
-
-
-
-
-From Source — Rust (primary) or Python
-
-```bash
-git clone https://github.com/ruvnet/wifi-densepose.git
-cd wifi-densepose
-
-# Rust (primary — 810x faster)
-cd rust-port/wifi-densepose-rs
-cargo build --release
-cargo test --workspace
-
-# Python (legacy v1)
-pip install -r requirements.txt
-pip install -e .
-
-# Or via pip
-pip install wifi-densepose
-pip install wifi-densepose[gpu] # GPU acceleration
-pip install wifi-densepose[all] # All optional deps
-```
-
-
-
-
-Docker — Pre-built images, no toolchain needed
-
-```bash
-# Rust sensing server (132 MB — recommended)
-docker pull ruvnet/wifi-densepose:latest
-docker run -p 3000:3000 -p 3001:3001 -p 5005:5005/udp ruvnet/wifi-densepose:latest
-
-# Python sensing pipeline (569 MB)
-docker pull ruvnet/wifi-densepose:python
-docker run -p 8765:8765 -p 8080:8080 ruvnet/wifi-densepose:python
-
-# Both via docker-compose
-cd docker && docker compose up
-
-# Export RVF model
-docker run --rm -v $(pwd):/out ruvnet/wifi-densepose:latest --export-rvf /out/model.rvf
-```
-
-| Image | Tag | Size | Ports |
-|-------|-----|------|-------|
-| `ruvnet/wifi-densepose` | `latest`, `rust` | 132 MB | 3000 (REST), 3001 (WS), 5005/udp (ESP32) |
-| `ruvnet/wifi-densepose` | `python` | 569 MB | 8765 (WS), 8080 (UI) |
-
-
-
-
-System Requirements
-
-- **Rust**: 1.70+ (primary runtime — install via [rustup](https://rustup.rs/))
-- **Python**: 3.8+ (for verification and legacy v1 API)
-- **OS**: Linux (Ubuntu 18.04+), macOS (10.15+), Windows 10+
-- **Memory**: Minimum 4GB RAM, Recommended 8GB+
-- **Storage**: 2GB free space for models and data
-- **Network**: WiFi interface with CSI capability (optional — installer detects what you have)
-- **GPU**: Optional (NVIDIA CUDA or Apple Metal)
-
-
-
-
-Rust Crates — Individual crates on crates.io
-
-The Rust workspace consists of 14 crates, all published to [crates.io](https://crates.io/):
-
-```bash
-# Add individual crates to your Cargo.toml
-cargo add wifi-densepose-core # Types, traits, errors
-cargo add wifi-densepose-signal # CSI signal processing (6 SOTA algorithms)
-cargo add wifi-densepose-nn # Neural inference (ONNX, PyTorch, Candle)
-cargo add wifi-densepose-vitals # Vital sign extraction (breathing + heart rate)
-cargo add wifi-densepose-mat # Disaster response (MAT survivor detection)
-cargo add wifi-densepose-hardware # ESP32, Intel 5300, Atheros sensors
-cargo add wifi-densepose-train # Training pipeline (MM-Fi dataset)
-cargo add wifi-densepose-wifiscan # Multi-BSSID WiFi scanning
-```
-
-| Crate | Description | RuVector | crates.io |
-|-------|-------------|----------|-----------|
-| [`wifi-densepose-core`](https://crates.io/crates/wifi-densepose-core) | Foundation types, traits, and utilities | -- | [](https://crates.io/crates/wifi-densepose-core) |
-| [`wifi-densepose-signal`](https://crates.io/crates/wifi-densepose-signal) | SOTA CSI signal processing (SpotFi, FarSense, Widar 3.0) | `mincut`, `attn-mincut`, `attention`, `solver` | [](https://crates.io/crates/wifi-densepose-signal) |
-| [`wifi-densepose-nn`](https://crates.io/crates/wifi-densepose-nn) | Multi-backend inference (ONNX, PyTorch, Candle) | -- | [](https://crates.io/crates/wifi-densepose-nn) |
-| [`wifi-densepose-train`](https://crates.io/crates/wifi-densepose-train) | Training pipeline with MM-Fi dataset (NeurIPS 2023) | **All 5** | [](https://crates.io/crates/wifi-densepose-train) |
-| [`wifi-densepose-mat`](https://crates.io/crates/wifi-densepose-mat) | Mass Casualty Assessment Tool (disaster survivor detection) | `solver`, `temporal-tensor` | [](https://crates.io/crates/wifi-densepose-mat) |
-| [`wifi-densepose-vitals`](https://crates.io/crates/wifi-densepose-vitals) | Vital signs: breathing (6-30 BPM), heart rate (40-120 BPM) | -- | [](https://crates.io/crates/wifi-densepose-vitals) |
-| [`wifi-densepose-hardware`](https://crates.io/crates/wifi-densepose-hardware) | ESP32, Intel 5300, Atheros CSI sensor interfaces | -- | [](https://crates.io/crates/wifi-densepose-hardware) |
-| [`wifi-densepose-wifiscan`](https://crates.io/crates/wifi-densepose-wifiscan) | Multi-BSSID WiFi scanning (Windows-enhanced) | -- | [](https://crates.io/crates/wifi-densepose-wifiscan) |
-| [`wifi-densepose-wasm`](https://crates.io/crates/wifi-densepose-wasm) | WebAssembly bindings for browser deployment | -- | [](https://crates.io/crates/wifi-densepose-wasm) |
-| [`wifi-densepose-sensing-server`](https://crates.io/crates/wifi-densepose-sensing-server) | Axum server: UDP ingestion, WebSocket broadcast | -- | [](https://crates.io/crates/wifi-densepose-sensing-server) |
-| [`wifi-densepose-cli`](https://crates.io/crates/wifi-densepose-cli) | Command-line tool for MAT disaster scanning | -- | [](https://crates.io/crates/wifi-densepose-cli) |
-| [`wifi-densepose-api`](https://crates.io/crates/wifi-densepose-api) | REST + WebSocket API layer | -- | [](https://crates.io/crates/wifi-densepose-api) |
-| [`wifi-densepose-config`](https://crates.io/crates/wifi-densepose-config) | Configuration management | -- | [](https://crates.io/crates/wifi-densepose-config) |
-| [`wifi-densepose-db`](https://crates.io/crates/wifi-densepose-db) | Database persistence (PostgreSQL, SQLite, Redis) | -- | [](https://crates.io/crates/wifi-densepose-db) |
-
-All crates integrate with [RuVector v2.0.4](https://github.com/ruvnet/ruvector) for graph algorithms and neural network optimization.
-
-
-
----
-
-## 🚀 Quick Start
-
-
-First API call in 3 commands
-
-### 1. Install
-
-```bash
-# Fastest path — Docker
-docker pull ruvnet/wifi-densepose:latest
-docker run -p 3000:3000 ruvnet/wifi-densepose:latest
-
-# Or from source (Rust)
-./install.sh --profile rust --yes
-```
-
-### 2. Start the System
+### 1. Core CSI Parsing Returns Random Data (Fatal)
+**`v1/src/hardware/csi_extractor.py` L83-84** — `ESP32CSIParser.parse()`:
```python
-from wifi_densepose import WiFiDensePose
-
-system = WiFiDensePose()
-system.start()
-poses = system.get_latest_poses()
-print(f"Detected {len(poses)} persons")
-system.stop()
+# Instead of parsing actual CSI bytes, generates random arrays
+amplitude = np.random.rand(num_antennas, num_subcarriers)
+phase = np.random.rand(num_antennas, num_subcarriers)
```
-### 3. REST API
-
-```bash
-# Health check
-curl http://localhost:3000/health
-
-# Latest sensing frame
-curl http://localhost:3000/api/v1/sensing/latest
-
-# Vital signs
-curl http://localhost:3000/api/v1/vital-signs
-
-# Pose estimation
-curl http://localhost:3000/api/v1/pose/current
-
-# Server info
-curl http://localhost:3000/api/v1/info
-```
-
-### 4. Real-time WebSocket
-
+**`v1/src/hardware/csi_extractor.py` L128-142** — `RouterCSIParser._parse_atheros_format()`:
```python
-import asyncio, websockets, json
-
-async def stream():
- async with websockets.connect("ws://localhost:3001/ws/sensing") as ws:
- async for msg in ws:
- data = json.loads(msg)
- print(f"Persons: {len(data.get('persons', []))}")
-
-asyncio.run(stream())
+# Returns entirely random mock data, labeled "placeholder implementation"
+amplitude=np.random.rand(3, 56),
+phase=np.random.rand(3, 56),
```
-
-
----
-
-## 📋 Table of Contents
-
-
-📡 Signal Processing & Sensing — From raw WiFi frames to vital signs
-
-The signal processing stack transforms raw WiFi Channel State Information into actionable human sensing data. Starting from 56-192 subcarrier complex values captured at 20 Hz, the pipeline applies research-grade algorithms (SpotFi phase correction, Hampel outlier rejection, Fresnel zone modeling) to extract breathing rate, heart rate, motion level, and multi-person body pose — all in pure Rust with zero external ML dependencies.
-
-| Section | Description | Docs |
-|---------|-------------|------|
-| [Key Features](#key-features) | Privacy-first sensing, real-time performance, multi-person tracking, Docker | — |
-| [ESP32-S3 Hardware Pipeline](#esp32-s3-hardware-pipeline) | 20 Hz CSI streaming, binary frame parsing, flash & provision | [ADR-018](docs/adr/ADR-018-esp32-dev-implementation.md) · [Tutorial #34](https://github.com/ruvnet/wifi-densepose/issues/34) |
-| [Vital Sign Detection](#vital-sign-detection) | Breathing 6-30 BPM, heartbeat 40-120 BPM, FFT peak detection | [ADR-021](docs/adr/ADR-021-vital-sign-detection-rvdna-pipeline.md) |
-| [WiFi Scan Domain Layer](#wifi-scan-domain-layer) | 8-stage RSSI pipeline, multi-BSSID fingerprinting, Windows WiFi | [ADR-022](docs/adr/ADR-022-windows-wifi-enhanced-fidelity-ruvector.md) · [Tutorial #36](https://github.com/ruvnet/wifi-densepose/issues/36) |
-| [WiFi-Mat Disaster Response](#wifi-mat-disaster-response) | Search & rescue, START triage, 3D localization through debris | [ADR-001](docs/adr/ADR-001-wifi-mat-disaster-detection.md) · [User Guide](docs/wifi-mat-user-guide.md) |
-| [SOTA Signal Processing](#sota-signal-processing) | SpotFi, Hampel, Fresnel, STFT spectrogram, subcarrier selection, BVP | [ADR-014](docs/adr/ADR-014-sota-signal-processing.md) |
-
-
-
-
-🧠 Models & Training — DensePose pipeline, RVF containers, SONA adaptation, RuVector integration
-
-The neural pipeline uses a graph transformer with cross-attention to map CSI feature matrices to 17 COCO body keypoints and DensePose UV coordinates. Models are packaged as single-file `.rvf` containers with progressive loading (Layer A instant, Layer B warm, Layer C full). SONA (Self-Optimizing Neural Architecture) enables continuous on-device adaptation via micro-LoRA + EWC++ without catastrophic forgetting. Signal processing is powered by 5 [RuVector](https://github.com/ruvnet/ruvector) crates (v2.0.4) with 7 integration points across the Rust workspace, plus 6 additional vendored crates for inference and graph intelligence.
-
-| Section | Description | Docs |
-|---------|-------------|------|
-| [RVF Model Container](#rvf-model-container) | Binary packaging with Ed25519 signing, progressive 3-layer loading, SIMD quantization | [ADR-023](docs/adr/ADR-023-trained-densepose-model-ruvector-pipeline.md) |
-| [Training & Fine-Tuning](#training--fine-tuning) | 8-phase pure Rust pipeline (7,832 lines), MM-Fi/Wi-Pose pre-training, 6-term composite loss, SONA LoRA | [ADR-023](docs/adr/ADR-023-trained-densepose-model-ruvector-pipeline.md) |
-| [RuVector Crates](#ruvector-crates) | 11 vendored Rust crates from [ruvector](https://github.com/ruvnet/ruvector): attention, min-cut, solver, GNN, HNSW, temporal compression, sparse inference | [GitHub](https://github.com/ruvnet/ruvector) · [Source](vendor/ruvector/) |
-
-
-
-
-🖥️ Usage & Configuration — CLI flags, API endpoints, hardware setup
-
-The Rust sensing server is the primary interface, offering a comprehensive CLI with flags for data source selection, model loading, training, benchmarking, and RVF export. A REST API (Axum) and WebSocket server provide real-time data access. The Python v1 CLI remains available for legacy workflows.
-
-| Section | Description | Docs |
-|---------|-------------|------|
-| [CLI Usage](#cli-usage) | `--source`, `--train`, `--benchmark`, `--export-rvf`, `--model`, `--progressive` | — |
-| [REST API & WebSocket](#rest-api--websocket) | 6 REST endpoints (sensing, vitals, BSSID, SONA), WebSocket real-time stream | — |
-| [Hardware Support](#hardware-support-1) | ESP32-S3 ($8), Intel 5300 ($15), Atheros AR9580 ($20), Windows RSSI ($0) | [ADR-012](docs/adr/ADR-012-esp32-csi-sensor-mesh.md) · [ADR-013](docs/adr/ADR-013-feature-level-sensing-commodity-gear.md) |
-
-
-
-
-⚙️ Development & Testing — 542+ tests, CI, deployment
-
-The project maintains 542+ pure-Rust tests across 7 crate suites with zero mocks — every test runs against real algorithm implementations. Hardware-free simulation mode (`--source simulate`) enables full-stack testing without physical devices. Docker images are published on Docker Hub for zero-setup deployment.
-
-| Section | Description | Docs |
-|---------|-------------|------|
-| [Testing](#testing) | 7 test suites: sensing-server (229), signal (83), mat (139), wifiscan (91), RVF (16), vitals (18) | — |
-| [Deployment](#deployment) | Docker images (132 MB Rust / 569 MB Python), docker-compose, env vars | — |
-| [Contributing](#contributing) | Fork → branch → test → PR workflow, Rust and Python dev setup | — |
-
-
-
-
-📊 Performance & Benchmarks — Measured throughput, latency, resource usage
-
-All benchmarks are measured on the Rust sensing server using `cargo bench` and the built-in `--benchmark` CLI flag. The Rust v2 implementation delivers 810x end-to-end speedup over the Python v1 baseline, with motion detection reaching 5,400x improvement. The vital sign detector processes 11,665 frames/second in a single-threaded benchmark.
-
-| Section | Description | Key Metric |
-|---------|-------------|------------|
-| [Performance Metrics](#performance-metrics) | Vital signs, CSI pipeline, motion detection, Docker image, memory | 11,665 fps vitals · 54K fps pipeline |
-| [Rust vs Python](#python-vs-rust) | Side-by-side benchmarks across 5 operations | **810x** full pipeline speedup |
-
-
-
-
-📄 Meta — License, changelog, support
-
-WiFi DensePose is MIT-licensed open source, developed by [ruvnet](https://github.com/ruvnet). The project has been in active development since March 2025, with 3 major releases delivering the Rust port, SOTA signal processing, disaster response module, and end-to-end training pipeline.
-
-| Section | Description | Link |
-|---------|-------------|------|
-| [Changelog](#changelog) | v2.3.0 (training pipeline + Docker), v2.2.0 (SOTA + WiFi-Mat), v2.1.0 (Rust port) | — |
-| [License](#license) | MIT License | [LICENSE](LICENSE) |
-| [Support](#support) | Bug reports, feature requests, community discussion | [Issues](https://github.com/ruvnet/wifi-densepose/issues) · [Discussions](https://github.com/ruvnet/wifi-densepose/discussions) |
-
-
-
----
-
-## 📡 Signal Processing & Sensing
-
-
-📡 ESP32-S3 Hardware Pipeline (ADR-018) — 20 Hz CSI streaming, flash & provision
-
-```
-ESP32-S3 (STA + promiscuous) UDP/5005 Rust aggregator
-┌─────────────────────────┐ ──────────> ┌──────────────────┐
-│ WiFi CSI callback 20 Hz │ ADR-018 │ Esp32CsiParser │
-│ ADR-018 binary frames │ binary │ CsiFrame output │
-│ stream_sender (UDP) │ │ presence detect │
-└─────────────────────────┘ └──────────────────┘
+**`v1/src/hardware/csi_extractor.py` L323-326** — `_read_raw_data()`:
+```python
+# Returns a HARDCODED string instead of reading from actual hardware
+return b"CSI_DATA:1234567890,3,56,2400,20,15.5,[1.0,2.0,3.0],[0.5,1.5,2.5]"
```
-| Metric | Measured |
-|--------|----------|
-| Frame rate | ~20 Hz sustained |
-| Subcarriers | 64 / 128 / 192 (LLTF, HT, HT40) |
-| Latency | < 1ms (UDP loopback) |
-| Presence detection | Motion score 10/10 at 3m |
+### 2. Feature Extraction Also Fake
-```bash
-# Pre-built binaries — no toolchain required
-# https://github.com/ruvnet/wifi-densepose/releases/tag/v0.1.0-esp32
-
-python -m esptool --chip esp32s3 --port COM7 --baud 460800 \
- write-flash --flash-mode dio --flash-size 4MB \
- 0x0 bootloader.bin 0x8000 partition-table.bin 0x10000 esp32-csi-node.bin
-
-python scripts/provision.py --port COM7 \
- --ssid "YourWiFi" --password "secret" --target-ip 192.168.1.20
-
-cargo run -p wifi-densepose-hardware --bin aggregator -- --bind 0.0.0.0:5005 --verbose
+**`v1/src/core/csi_processor.py` L390** — `_extract_doppler_features()`:
+```python
+# "Doppler estimation" is literally random noise
+doppler_shift = np.random.rand(10) # Placeholder
```
-See [firmware/esp32-csi-node/README.md](firmware/esp32-csi-node/README.md) and [Tutorial #34](https://github.com/ruvnet/wifi-densepose/issues/34).
+### 3. Neural Networks: Architecture Without Soul
-
+- `DensePoseHead` and `ModalityTranslationNetwork` have valid PyTorch architecture code
+- But there are **NO pretrained weights, NO training scripts, NO dataset, NO evaluation code**
+- An untrained neural network cannot produce the claimed "94.2% pose detection accuracy"
-
-🦀 Rust Implementation (v2) — 810x faster, 54K fps pipeline
+### 4. Fabricated Claims
-### Performance Benchmarks (Validated)
+The original README claims:
-| Operation | Python (v1) | Rust (v2) | Speedup |
-|-----------|-------------|-----------|---------|
-| CSI Preprocessing (4x64) | ~5ms | **5.19 µs** | ~1000x |
-| Phase Sanitization (4x64) | ~3ms | **3.84 µs** | ~780x |
-| Feature Extraction (4x64) | ~8ms | **9.03 µs** | ~890x |
-| Motion Detection | ~1ms | **186 ns** | ~5400x |
-| **Full Pipeline** | ~15ms | **18.47 µs** | ~810x |
-| **Vital Signs** | N/A | **86 µs** | 11,665 fps |
-
-| Resource | Python (v1) | Rust (v2) |
-|----------|-------------|-----------|
-| Memory | ~500 MB | ~100 MB |
-| Docker Image | 569 MB | 132 MB |
-| Tests | 41 | 542+ |
-| WASM Support | No | Yes |
-
-```bash
-cd rust-port/wifi-densepose-rs
-cargo build --release
-cargo test --workspace
-cargo bench --package wifi-densepose-signal
-```
-
-
-
-
-💓 Vital Sign Detection (ADR-021) — Breathing and heartbeat via FFT
-
-| Capability | Range | Method |
-|------------|-------|--------|
-| **Breathing Rate** | 6-30 BPM (0.1-0.5 Hz) | Bandpass filter + FFT peak detection |
-| **Heart Rate** | 40-120 BPM (0.8-2.0 Hz) | Bandpass filter + FFT peak detection |
-| **Sampling Rate** | 20 Hz (ESP32 CSI) | Real-time streaming |
-| **Confidence** | 0.0-1.0 per sign | Spectral coherence + signal quality |
-
-```bash
-./target/release/sensing-server --source simulate --http-port 3000 --ws-port 3001 --ui-path ../../ui
-curl http://localhost:3000/api/v1/vital-signs
-```
-
-See [ADR-021](docs/adr/ADR-021-vital-sign-detection-rvdna-pipeline.md).
-
-
-
-
-📡 WiFi Scan Domain Layer (ADR-022) — 8-stage RSSI pipeline for Windows WiFi
-
-| Stage | Purpose |
+| Claim | Reality |
|-------|---------|
-| **Predictive Gating** | Pre-filter scan results using temporal prediction |
-| **Attention Weighting** | Weight BSSIDs by signal relevance |
-| **Spatial Correlation** | Cross-AP spatial signal correlation |
-| **Motion Estimation** | Detect movement from RSSI variance |
-| **Breathing Extraction** | Extract respiratory rate from sub-Hz oscillations |
-| **Quality Gating** | Reject low-confidence estimates |
-| **Fingerprint Matching** | Location and posture classification via RF fingerprints |
-| **Orchestration** | Fuse all stages into unified sensing output |
+| "100% test coverage" | Tests pass because they test random-number generators |
+| "94.2% pose detection accuracy" | Impossible without a trained model |
+| "96.5% fall detection sensitivity" | No working detection pipeline exists |
+| Docker image `ruvnet/wifi-densepose:latest` | Did not exist on Docker Hub (confirmed by Issue #3) |
-```bash
-cargo test -p wifi-densepose-wifiscan
-```
+### 5. Suspicious Project History
-See [ADR-022](docs/adr/ADR-022-windows-wifi-enhanced-fidelity-ruvector.md) and [Tutorial #36](https://github.com/ruvnet/wifi-densepose/issues/36).
+| Metric | Value | Red Flag |
+|--------|-------|----------|
+| Stars | 8,365+ | Extremely high for a non-functional project |
+| Total commits | ~35 | ~239 stars per commit |
+| Contributors | 2 | Author + "claude" bot |
+| Project creation | 2025-06-07 | Most code committed in a single day |
+| Commit `5101504` | — | Message literally says *"I've successfully completed a full review of the WiFi-DensePose system"* |
+| Last commit | 2026-01-14 | Titled "Make Python implementation real - remove random data generators" — **author admitted core was fake** |
+| Star growth | Issue #12 | "1.3k to 3k+ overnight with no commits in 6 months" |
-
+### 6. Issue Suppression
-
-🚨 WiFi-Mat: Disaster Response — Search & rescue, START triage, 3D localization
-
-WiFi signals penetrate non-metallic debris (concrete, wood, drywall) where cameras and thermal sensors cannot reach. The WiFi-Mat module (`wifi-densepose-mat`, 139 tests) uses CSI analysis to detect survivors trapped under rubble, classify their condition using the START triage protocol, and estimate their 3D position — giving rescue teams actionable intelligence within seconds of deployment.
-
-| Capability | How It Works | Performance Target |
-|------------|-------------|-------------------|
-| **Breathing Detection** | Bandpass 0.07-1.0 Hz + Fresnel zone modeling detects chest displacement of 5-10mm at 5 GHz | 4-60 BPM, <500ms latency |
-| **Heartbeat Detection** | Micro-Doppler shift extraction from fine-grained CSI phase variation | Via ruvector-temporal-tensor |
-| **3D Localization** | Multi-AP triangulation + CSI fingerprint matching + depth estimation through rubble layers | 3-5m penetration |
-| **START Triage** | Ensemble classifier votes on breathing + movement + vital stability → P1-P4 priority | <1% false negative |
-| **Zone Scanning** | 16+ concurrent scan zones with periodic re-scan and audit logging | Full disaster site |
-
-**Triage classification (START protocol compatible):**
-
-| Status | Color | Detection Criteria | Priority |
-|--------|-------|-------------------|----------|
-| Immediate | Red | Breathing detected, no movement | P1 |
-| Delayed | Yellow | Movement + breathing, stable vitals | P2 |
-| Minor | Green | Strong movement, responsive patterns | P3 |
-| Deceased | Black | No vitals for >30 min continuous scan | P4 |
-
-**Deployment modes:** portable (single TX/RX handheld), distributed (multiple APs around collapse site), drone-mounted (UAV scanning), vehicle-mounted (mobile command post).
-
-```rust
-use wifi_densepose_mat::{DisasterResponse, DisasterConfig, DisasterType, ScanZone, ZoneBounds};
-
-let config = DisasterConfig::builder()
- .disaster_type(DisasterType::Earthquake)
- .sensitivity(0.85)
- .max_depth(5.0)
- .build();
-
-let mut response = DisasterResponse::new(config);
-response.initialize_event(location, "Building collapse")?;
-response.add_zone(ScanZone::new("North Wing", ZoneBounds::rectangle(0.0, 0.0, 30.0, 20.0)))?;
-response.start_scanning().await?;
-```
-
-**Safety guarantees:** fail-safe defaults (assume life present on ambiguous signals), redundant multi-algorithm voting, complete audit trail, offline-capable (no network required).
-
-- [WiFi-Mat User Guide](docs/wifi-mat-user-guide.md) | [ADR-001](docs/adr/ADR-001-wifi-mat-disaster-detection.md) | [Domain Model](docs/ddd/wifi-mat-domain-model.md)
-
-
-
-
-🔬 SOTA Signal Processing (ADR-014) — 6 research-grade algorithms
-
-The signal processing layer bridges the gap between raw commodity WiFi hardware output and research-grade sensing accuracy. Each algorithm addresses a specific limitation of naive CSI processing — from hardware-induced phase corruption to environment-dependent multipath interference. All six are implemented in `wifi-densepose-signal/src/` with deterministic tests and no mock data.
-
-| Algorithm | What It Does | Why It Matters | Math | Source |
-|-----------|-------------|----------------|------|--------|
-| **Conjugate Multiplication** | Multiplies CSI antenna pairs: `H₁[k] × conj(H₂[k])` | Cancels CFO, SFO, and packet detection delay that corrupt raw phase — preserves only environment-caused phase differences | `CSI_ratio[k] = H₁[k] * conj(H₂[k])` | [SpotFi](https://dl.acm.org/doi/10.1145/2789168.2790124) (SIGCOMM 2015) |
-| **Hampel Filter** | Replaces outliers using running median ± scaled MAD | Z-score uses mean/std which are corrupted by the very outliers it detects (masking effect). Hampel uses median/MAD, resisting up to 50% contamination | `σ̂ = 1.4826 × MAD` | Standard DSP; WiGest (2015) |
-| **Fresnel Zone Model** | Models signal variation from chest displacement crossing Fresnel zone boundaries | Zero-crossing counting fails in multipath-rich environments. Fresnel predicts *where* breathing should appear based on TX-RX-body geometry | `ΔΦ = 2π × 2Δd / λ`, `A = \|sin(ΔΦ/2)\|` | [FarSense](https://dl.acm.org/doi/10.1145/3300061.3345431) (MobiCom 2019) |
-| **CSI Spectrogram** | Sliding-window FFT (STFT) per subcarrier → 2D time-frequency matrix | Breathing = 0.2-0.4 Hz band, walking = 1-2 Hz, static = noise. 2D structure enables CNN spatial pattern recognition that 1D features miss | `S[t,f] = \|Σₙ x[n] w[n-t] e^{-j2πfn}\|²` | Standard since 2018 |
-| **Subcarrier Selection** | Ranks subcarriers by motion sensitivity (variance ratio) and selects top-K | Not all subcarriers respond to motion — some sit in multipath nulls. Selecting the 10-20 most sensitive improves SNR by 6-10 dB | `sensitivity[k] = var_motion / var_static` | [WiDance](https://dl.acm.org/doi/10.1145/3117811.3117826) (MobiCom 2017) |
-| **Body Velocity Profile** | Extracts velocity distribution from Doppler shifts across subcarriers | BVP is domain-independent — same velocity profile regardless of room layout, furniture, or AP placement. Basis for cross-environment recognition | `BVP[v,t] = Σₖ \|STFTₖ[v,t]\|` | [Widar 3.0](https://dl.acm.org/doi/10.1145/3328916) (MobiSys 2019) |
-
-**Processing pipeline order:** Raw CSI → Conjugate multiplication (phase cleaning) → Hampel filter (outlier removal) → Subcarrier selection (top-K) → CSI spectrogram (time-frequency) → Fresnel model (breathing) + BVP (activity)
-
-See [ADR-014](docs/adr/ADR-014-sota-signal-processing.md) for full mathematical derivations.
-
-
+All prior issues reporting these problems (#3, #5, #6, #7, #8, #9, #11, #12, #13, #14, #15, #16) were closed as "not planned" without addressing the technical concerns. Issue #29 (this audit) was **deleted entirely**.
---
-## 🧠 Models & Training
+## Cross-Verification Results
-
-📦 RVF Model Container — Single-file deployment with progressive loading
+This audit was independently verified by three AI systems:
-The [RuVector Format (RVF)](https://github.com/ruvnet/ruvector/tree/main/crates/rvf) packages an entire trained model — weights, HNSW indexes, quantization codebooks, SONA adaptation deltas, and WASM inference runtime — into a single self-contained binary file. No external dependencies are needed at deployment time.
-
-**Container structure:**
-
-```
-┌──────────────────────────────────────────────────────┐
-│ RVF Container (.rvf) │
-│ │
-│ ┌─────────────┐ 64-byte header per segment │
-│ │ Manifest │ Magic: 0x52564653 ("RVFS") │
-│ ├─────────────┤ Type + content hash + compression │
-│ │ Weights │ Model parameters (f32/f16/u8) │
-│ ├─────────────┤ │
-│ │ HNSW Index │ Vector search index │
-│ ├─────────────┤ │
-│ │ Quant │ Quantization codebooks │
-│ ├─────────────┤ │
-│ │ SONA Profile │ LoRA deltas + EWC++ Fisher matrix │
-│ ├─────────────┤ │
-│ │ Witness │ Ed25519 training proof │
-│ ├─────────────┤ │
-│ │ Vitals Config│ Breathing/HR filter parameters │
-│ └─────────────┘ │
-└──────────────────────────────────────────────────────┘
-```
-
-**Deployment targets:**
-
-| Target | Quantization | Size | Load Time | Use Case |
-|--------|-------------|------|-----------|----------|
-| **ESP32 / IoT** | int4 | ~0.7 MB | <5ms (Layer A) | Presence + breathing only |
-| **Mobile / WebView** | int8 | ~6 MB | ~200ms (Layer B) | Pose estimation on phone |
-| **Browser (WASM)** | int8 | ~10 MB | ~500ms (Layer B) | In-browser demo |
-| **Field (WiFi-Mat)** | fp16 | ~62 MB | ~2s (Layer C) | Full DensePose + disaster triage |
-| **Server / Cloud** | f32 | ~50+ MB | ~3s (Layer C) | Training + full inference |
-
-| Property | Detail |
-|----------|--------|
-| **Format** | Segment-based binary, 20+ segment types, CRC32 integrity per segment |
-| **Progressive Loading** | **Layer A** (<5ms): manifest + entry points → **Layer B** (100ms-1s): hot weights + adjacency → **Layer C** (seconds): full graph |
-| **Signing** | Ed25519 training proofs for verifiable provenance — chain of custody from training data to deployed model |
-| **Quantization** | Per-segment temperature-tiered: f32 (full), f16 (half), u8 (int8), int4 — with SIMD-accelerated distance computation |
-| **CLI** | `--export-rvf` (generate), `--load-rvf` (config), `--save-rvf` (persist), `--model` (inference), `--progressive` (3-layer load) |
-
-```bash
-# Export model package
-./target/release/sensing-server --export-rvf wifi-densepose-v1.rvf
-
-# Load and run with progressive loading
-./target/release/sensing-server --model wifi-densepose-v1.rvf --progressive
-
-# Export via Docker
-docker run --rm -v $(pwd):/out ruvnet/wifi-densepose:latest --export-rvf /out/model.rvf
-```
-
-Built on the [rvf](https://github.com/ruvnet/ruvector/tree/main/crates/rvf) crate family (rvf-types, rvf-wire, rvf-manifest, rvf-index, rvf-quant, rvf-crypto, rvf-runtime). See [ADR-023](docs/adr/ADR-023-trained-densepose-model-ruvector-pipeline.md).
-
-
-
-
-🧬 Training & Fine-Tuning — MM-Fi/Wi-Pose pre-training, SONA adaptation
-
-The training pipeline implements 8 phases in pure Rust (7,832 lines, zero external ML dependencies). It trains a graph transformer with cross-attention to map CSI feature matrices to 17 COCO body keypoints and DensePose UV coordinates — following the approach from the CMU "DensePose From WiFi" paper ([arXiv:2301.00250](https://arxiv.org/abs/2301.00250)). RuVector crates provide the core building blocks: [ruvector-attention](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-attention) for cross-attention layers, [ruvector-mincut](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-mincut) for multi-person matching, and [ruvector-temporal-tensor](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-temporal-tensor) for CSI buffer compression.
-
-**Three-tier data strategy:**
-
-| Tier | Method | Purpose | RuVector Integration |
-|------|--------|---------|---------------------|
-| **1. Pre-train** | MM-Fi + Wi-Pose public datasets | Cross-environment generalization (multi-subject, multi-room) | `ruvector-temporal-tensor` compresses CSI windows (114→56 subcarrier resampling) |
-| **2. Fine-tune** | ESP32 CSI + camera pseudo-labels | Environment-specific multipath adaptation | `ruvector-solver` for Fresnel geometry, `ruvector-attn-mincut` for subcarrier gating |
-| **3. SONA adapt** | Micro-LoRA (rank-4) + EWC++ | Continuous on-device learning without catastrophic forgetting | [SONA](https://github.com/ruvnet/ruvector/tree/main/crates/sona) architecture (Self-Optimizing Neural Architecture) |
-
-**Training pipeline components:**
-
-| Phase | Module | What It Does | RuVector Crate |
-|-------|--------|-------------|----------------|
-| 1 | `dataset.rs` (850 lines) | MM-Fi `.npy` + Wi-Pose `.mat` loaders, subcarrier resampling (114→56, 30→56), windowing | [ruvector-temporal-tensor](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-temporal-tensor) |
-| 2 | `graph_transformer.rs` (855 lines) | COCO BodyGraph (17 kp, 16 edges), AntennaGraph, multi-head CrossAttention, GCN message passing | [ruvector-attention](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-attention) |
-| 3 | `trainer.rs` (881 lines) | 6-term composite loss (MSE, CE, UV, temporal, bone, symmetry), SGD+momentum, cosine+warmup, PCK/OKS | [ruvector-mincut](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-mincut) (person matching) |
-| 4 | `sona.rs` (639 lines) | LoRA adapters (A×B delta), EWC++ Fisher regularization, EnvironmentDetector (3-sigma drift) | [sona](https://github.com/ruvnet/ruvector/tree/main/crates/sona) |
-| 5 | `sparse_inference.rs` (753 lines) | NeuronProfiler hot/cold partitioning, SparseLinear (skip cold rows), INT8/FP16 quantization | [ruvector-sparse-inference](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-sparse-inference) |
-| 6 | `rvf_pipeline.rs` (1,027 lines) | Progressive 3-layer loader, HNSW index, OverlayGraph, `RvfModelBuilder` | [ruvector-core](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-core) (HNSW) |
-| 7 | `rvf_container.rs` (914 lines) | Binary container format, 6+ segment types, CRC32 integrity | [rvf](https://github.com/ruvnet/ruvector/tree/main/crates/rvf) |
-| 8 | `main.rs` integration | `--train`, `--model`, `--progressive` CLI flags, REST endpoints | — |
-
-**SONA (Self-Optimizing Neural Architecture)** — the continuous adaptation system:
-
-| Component | What It Does | Why It Matters |
-|-----------|-------------|----------------|
-| **Micro-LoRA (rank-4)** | Trains small A×B weight deltas instead of full weights | 100x fewer parameters to update → runs on ESP32 |
-| **EWC++ (Fisher matrix)** | Penalizes changes to important weights from previous environments | Prevents catastrophic forgetting when moving between rooms |
-| **EnvironmentDetector** | Monitors CSI feature drift with 3-sigma threshold | Auto-triggers adaptation when the model is moved to a new space |
-| **Best-epoch snapshot** | Saves best validation loss weights, restores before export | Prevents shipping overfit final-epoch parameters |
-
-```bash
-# Pre-train on MM-Fi dataset
-./target/release/sensing-server --train --dataset data/ --dataset-type mmfi --epochs 100
-
-# Train and export to RVF in one step
-./target/release/sensing-server --train --dataset data/ --epochs 100 --save-rvf model.rvf
-
-# Via Docker (no toolchain needed)
-docker run --rm -v $(pwd)/data:/data ruvnet/wifi-densepose:latest \
- --train --dataset /data --epochs 100 --export-rvf /data/model.rvf
-```
-
-See [ADR-023](docs/adr/ADR-023-trained-densepose-model-ruvector-pipeline.md) · [SONA crate](https://github.com/ruvnet/ruvector/tree/main/crates/sona) · [arXiv:2301.00250](https://arxiv.org/abs/2301.00250)
-
-
-
-
-🔩 RuVector Crates — 11 vendored signal intelligence crates from github.com/ruvnet/ruvector
-
-**5 directly-used crates** (v2.0.4, declared in `Cargo.toml`, 7 integration points):
-
-| Crate | What It Does | Where It's Used in WiFi-DensePose | Source |
-|-------|-------------|-----------------------------------|--------|
-| [`ruvector-attention`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-attention) | Scaled dot-product attention, MoE routing, sparse attention | `model.rs` (spatial attention), `bvp.rs` (sensitivity-weighted velocity profiles) | [crate](https://crates.io/crates/ruvector-attention) |
-| [`ruvector-mincut`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-mincut) | Subpolynomial dynamic min-cut O(n^1.5 log n) | `metrics.rs` (DynamicPersonMatcher — multi-person assignment), `subcarrier_selection.rs` (sensitive/insensitive split) | [crate](https://crates.io/crates/ruvector-mincut) |
-| [`ruvector-attn-mincut`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-attn-mincut) | Attention-gated spectrogram noise suppression | `model.rs` (antenna attention gating), `spectrogram.rs` (gate noisy time-frequency bins) | [crate](https://crates.io/crates/ruvector-attn-mincut) |
-| [`ruvector-solver`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-solver) | Sparse Neumann series solver O(sqrt(n)) | `fresnel.rs` (TX-body-RX geometry), `triangulation.rs` (3D localization), `subcarrier.rs` (sparse interpolation 114→56) | [crate](https://crates.io/crates/ruvector-solver) |
-| [`ruvector-temporal-tensor`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-temporal-tensor) | Tiered temporal compression (8/7/5/3-bit) | `dataset.rs` (CSI buffer compression), `breathing.rs` + `heartbeat.rs` (compressed vital sign spectrograms) | [crate](https://crates.io/crates/ruvector-temporal-tensor) |
-
-**6 additional vendored crates** (used by training pipeline and inference):
-
-| Crate | What It Does | Source |
-|-------|-------------|--------|
-| [`ruvector-core`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-core) | VectorDB engine, HNSW index, SIMD distance functions, quantization codebooks | [crate](https://crates.io/crates/ruvector-core) |
-| [`ruvector-gnn`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-gnn) | Graph neural network layers, graph attention, EWC-regularized training | [crate](https://crates.io/crates/ruvector-gnn) |
-| [`ruvector-graph-transformer`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-graph-transformer) | Proof-gated graph transformer with cross-attention | [crate](https://crates.io/crates/ruvector-graph-transformer) |
-| [`ruvector-sparse-inference`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-sparse-inference) | PowerInfer-style hot/cold neuron partitioning, skip cold rows at runtime | [crate](https://crates.io/crates/ruvector-sparse-inference) |
-| [`ruvector-nervous-system`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-nervous-system) | PredictiveLayer, OscillatoryRouter, Hopfield associative memory | [crate](https://crates.io/crates/ruvector-nervous-system) |
-| [`ruvector-coherence`](https://github.com/ruvnet/ruvector/tree/main/crates/ruvector-coherence) | Spectral coherence monitoring, HNSW graph health, Fiedler connectivity | [crate](https://crates.io/crates/ruvector-coherence) |
-
-The full RuVector ecosystem includes 90+ crates. See [github.com/ruvnet/ruvector](https://github.com/ruvnet/ruvector) for the complete library, and [`vendor/ruvector/`](vendor/ruvector/) for the vendored source in this project.
-
-
+| Reviewer | Verdict | Score |
+|----------|---------|-------|
+| **Claude (Anthropic)** | AI-generated facade with placeholder core | **1/10** |
+| **Codex / GPT-5.2 (OpenAI)** | "Not a legitimate functional project. At best a scaffold with placeholders; at worst intentionally deceptive." | **1/10** |
+| **Gemini (Google)** | AI-generated non-functional fake project designed to manufacture influence | **0/10** |
---
-
-🏗️ System Architecture — End-to-end data flow from CSI capture to REST/WebSocket API
+## Clarification
-### End-to-End Pipeline
-
-```mermaid
-graph TB
- subgraph HW ["📡 Hardware Layer"]
- direction LR
- R1["WiFi Router 1
CSI Source"]
- R2["WiFi Router 2
CSI Source"]
- R3["WiFi Router 3
CSI Source"]
- ESP["ESP32-S3 Mesh
20 Hz · 56 subcarriers"]
- WIN["Windows WiFi
RSSI scanning"]
- end
-
- subgraph INGEST ["⚡ Ingestion"]
- AGG["Aggregator
UDP :5005 · ADR-018 frames"]
- BRIDGE["Bridge
I/Q → amplitude + phase"]
- end
-
- subgraph SIGNAL ["🔬 Signal Processing — RuVector v2.0.4"]
- direction TB
- PHASE["Phase Sanitization
SpotFi conjugate multiply"]
- HAMPEL["Hampel Filter
Outlier rejection · σ=3"]
- SUBSEL["Subcarrier Selection
ruvector-mincut · sensitive/insensitive split"]
- SPEC["Spectrogram
ruvector-attn-mincut · gated STFT"]
- FRESNEL["Fresnel Geometry
ruvector-solver · TX-body-RX distance"]
- BVP["Body Velocity Profile
ruvector-attention · weighted BVP"]
- end
-
- subgraph ML ["🧠 Neural Pipeline"]
- direction TB
- GRAPH["Graph Transformer
17 COCO keypoints · 16 edges"]
- CROSS["Cross-Attention
CSI features → body pose"]
- SONA["SONA Adapter
LoRA rank-4 · EWC++"]
- end
-
- subgraph VITAL ["💓 Vital Signs"]
- direction LR
- BREATH["Breathing
0.1–0.5 Hz · FFT peak"]
- HEART["Heart Rate
0.8–2.0 Hz · FFT peak"]
- MOTION["Motion Level
Variance + band power"]
- end
-
- subgraph API ["🌐 Output Layer"]
- direction LR
- REST["REST API
Axum :3000 · 6 endpoints"]
- WS["WebSocket
:3001 · real-time stream"]
- ANALYTICS["Analytics
Fall · Activity · START triage"]
- UI["Web UI
Three.js · Gaussian splats"]
- end
-
- R1 & R2 & R3 --> AGG
- ESP --> AGG
- WIN --> BRIDGE
- AGG --> BRIDGE
- BRIDGE --> PHASE
- PHASE --> HAMPEL
- HAMPEL --> SUBSEL
- SUBSEL --> SPEC
- SPEC --> FRESNEL
- FRESNEL --> BVP
- BVP --> GRAPH
- GRAPH --> CROSS
- CROSS --> SONA
- SONA --> BREATH & HEART & MOTION
- BREATH & HEART & MOTION --> REST & WS & ANALYTICS
- WS --> UI
-
- style HW fill:#1a1a2e,stroke:#e94560,color:#eee
- style INGEST fill:#16213e,stroke:#0f3460,color:#eee
- style SIGNAL fill:#0f3460,stroke:#533483,color:#eee
- style ML fill:#533483,stroke:#e94560,color:#eee
- style VITAL fill:#2d132c,stroke:#e94560,color:#eee
- style API fill:#1a1a2e,stroke:#0f3460,color:#eee
-```
-
-### Signal Processing Detail
-
-```mermaid
-graph LR
- subgraph RAW ["Raw CSI Frame"]
- IQ["I/Q Samples
56–192 subcarriers × N antennas"]
- end
-
- subgraph CLEAN ["Phase Cleanup"]
- CONJ["Conjugate Multiply
Remove carrier freq offset"]
- UNWRAP["Phase Unwrap
Remove 2π discontinuities"]
- HAMPEL2["Hampel Filter
Remove impulse noise"]
- end
-
- subgraph SELECT ["Subcarrier Intelligence"]
- MINCUT["Min-Cut Partition
ruvector-mincut"]
- GATE["Attention Gate
ruvector-attn-mincut"]
- end
-
- subgraph EXTRACT ["Feature Extraction"]
- STFT["STFT Spectrogram
Time-frequency decomposition"]
- FRESNELZ["Fresnel Zones
ruvector-solver"]
- BVPE["BVP Estimation
ruvector-attention"]
- end
-
- subgraph OUT ["Output Features"]
- AMP["Amplitude Matrix"]
- PHASE2["Phase Matrix"]
- DOPPLER["Doppler Shifts"]
- VITALS["Vital Band Power"]
- end
-
- IQ --> CONJ --> UNWRAP --> HAMPEL2
- HAMPEL2 --> MINCUT --> GATE
- GATE --> STFT --> FRESNELZ --> BVPE
- BVPE --> AMP & PHASE2 & DOPPLER & VITALS
-
- style RAW fill:#0d1117,stroke:#58a6ff,color:#c9d1d9
- style CLEAN fill:#161b22,stroke:#58a6ff,color:#c9d1d9
- style SELECT fill:#161b22,stroke:#d29922,color:#c9d1d9
- style EXTRACT fill:#161b22,stroke:#3fb950,color:#c9d1d9
- style OUT fill:#0d1117,stroke:#8b949e,color:#c9d1d9
-```
-
-### Deployment Topology
-
-```mermaid
-graph TB
- subgraph EDGE ["Edge (ESP32-S3 Mesh)"]
- E1["Node 1
Kitchen"]
- E2["Node 2
Living room"]
- E3["Node 3
Bedroom"]
- end
-
- subgraph SERVER ["Server (Rust · 132 MB Docker)"]
- SENSE["Sensing Server
:3000 REST · :3001 WS · :5005 UDP"]
- RVF["RVF Model
Progressive 3-layer load"]
- STORE["Time-Series Store
In-memory ring buffer"]
- end
-
- subgraph CLIENT ["Clients"]
- BROWSER["Browser
Three.js UI · Gaussian splats"]
- MOBILE["Mobile App
WebSocket stream"]
- DASH["Dashboard
REST polling"]
- IOT["Home Automation
MQTT bridge"]
- end
-
- E1 -->|"UDP :5005
ADR-018 frames"| SENSE
- E2 -->|"UDP :5005"| SENSE
- E3 -->|"UDP :5005"| SENSE
- SENSE <--> RVF
- SENSE <--> STORE
- SENSE -->|"WS :3001
real-time JSON"| BROWSER & MOBILE
- SENSE -->|"REST :3000
on-demand"| DASH & IOT
-
- style EDGE fill:#1a1a2e,stroke:#e94560,color:#eee
- style SERVER fill:#16213e,stroke:#533483,color:#eee
- style CLIENT fill:#0f3460,stroke:#0f3460,color:#eee
-```
-
-| Component | Crate / Module | Description |
-|-----------|---------------|-------------|
-| **Aggregator** | `wifi-densepose-hardware` | ESP32 UDP listener, ADR-018 frame parser, I/Q → amplitude/phase bridge |
-| **Signal Processor** | `wifi-densepose-signal` | SpotFi phase sanitization, Hampel filter, STFT spectrogram, Fresnel geometry, BVP |
-| **Subcarrier Selection** | `ruvector-mincut` + `ruvector-attn-mincut` | Dynamic sensitive/insensitive partitioning, attention-gated noise suppression |
-| **Fresnel Solver** | `ruvector-solver` | Sparse Neumann series O(sqrt(n)) for TX-body-RX distance estimation |
-| **Graph Transformer** | `wifi-densepose-train` | COCO BodyGraph (17 kp, 16 edges), cross-attention CSI→pose, GCN message passing |
-| **SONA** | `sona` crate | Micro-LoRA (rank-4) adaptation, EWC++ catastrophic forgetting prevention |
-| **Vital Signs** | `wifi-densepose-signal` | FFT-based breathing (0.1-0.5 Hz) and heartbeat (0.8-2.0 Hz) extraction |
-| **REST API** | `wifi-densepose-sensing-server` | Axum server: `/api/v1/sensing`, `/health`, `/vital-signs`, `/bssid`, `/sona` |
-| **WebSocket** | `wifi-densepose-sensing-server` | Real-time pose, sensing, and vital sign streaming on `:3001` |
-| **Analytics** | `wifi-densepose-mat` | Fall detection, activity recognition, START triage (WiFi-Mat disaster module) |
-| **Web UI** | `ui/` | Three.js scene, Gaussian splat visualization, signal dashboard |
-
-
+The underlying **CMU research** on WiFi-based DensePose ([paper](https://arxiv.org/abs/2301.00250)) is **legitimate and scientifically valid**. However, **this repository has no connection to that research** and does not implement any of its methods.
---
-## 🖥️ CLI Usage
+## Community Feedback (from original repo issues)
-
-Rust Sensing Server — Primary CLI interface
-
-```bash
-# Start with simulated data (no hardware)
-./target/release/sensing-server --source simulate --ui-path ../../ui
-
-# Start with ESP32 CSI hardware
-./target/release/sensing-server --source esp32 --udp-port 5005
-
-# Start with Windows WiFi RSSI
-./target/release/sensing-server --source wifi
-
-# Run vital sign benchmark
-./target/release/sensing-server --benchmark
-
-# Export RVF model package
-./target/release/sensing-server --export-rvf model.rvf
-
-# Train a model
-./target/release/sensing-server --train --dataset data/ --epochs 100
-
-# Load trained model with progressive loading
-./target/release/sensing-server --model wifi-densepose-v1.rvf --progressive
-```
-
-| Flag | Description |
-|------|-------------|
-| `--source` | Data source: `auto`, `wifi`, `esp32`, `simulate` |
-| `--http-port` | HTTP port for UI and REST API (default: 8080) |
-| `--ws-port` | WebSocket port (default: 8765) |
-| `--udp-port` | UDP port for ESP32 CSI frames (default: 5005) |
-| `--benchmark` | Run vital sign benchmark (1000 frames) and exit |
-| `--export-rvf` | Export RVF container package and exit |
-| `--load-rvf` | Load model config from RVF container |
-| `--save-rvf` | Save model state on shutdown |
-| `--model` | Load trained `.rvf` model for inference |
-| `--progressive` | Enable progressive loading (Layer A instant start) |
-| `--train` | Train a model and exit |
-| `--dataset` | Path to dataset directory (MM-Fi or Wi-Pose) |
-| `--epochs` | Training epochs (default: 100) |
-
-
-
-
-REST API & WebSocket — Endpoints reference
-
-#### REST API (Rust Sensing Server)
-
-```bash
-GET /api/v1/sensing # Latest sensing frame
-GET /api/v1/vital-signs # Breathing, heart rate, confidence
-GET /api/v1/bssid # Multi-BSSID registry
-GET /api/v1/model/layers # Progressive loading status
-GET /api/v1/model/sona/profiles # SONA profiles
-POST /api/v1/model/sona/activate # Activate SONA profile
-```
-
-WebSocket: `ws://localhost:3001/ws/sensing` (real-time sensing + vital signs)
-
-> Default ports (Docker): HTTP 3000, WS 3001. Binary defaults: HTTP 8080, WS 8765. Override with `--http-port` / `--ws-port`.
-
-
-
-
-Hardware Support — Devices, cost, and guides
-
-| Hardware | CSI | Cost | Guide |
-|----------|-----|------|-------|
-| **ESP32-S3** | Native | ~$8 | [Tutorial #34](https://github.com/ruvnet/wifi-densepose/issues/34) |
-| Intel 5300 | Firmware mod | ~$15 | Linux `iwl-csi` |
-| Atheros AR9580 | ath9k patch | ~$20 | Linux only |
-| Any Windows WiFi | RSSI only | $0 | [Tutorial #36](https://github.com/ruvnet/wifi-densepose/issues/36) |
-
-
-
-
-Python Legacy CLI — v1 API server commands
-
-```bash
-wifi-densepose start # Start API server
-wifi-densepose -c config.yaml start # Custom config
-wifi-densepose -v start # Verbose logging
-wifi-densepose status # Check status
-wifi-densepose stop # Stop server
-wifi-densepose config show # Show configuration
-wifi-densepose db init # Initialize database
-wifi-densepose tasks list # List background tasks
-```
-
-
-
-
-Documentation Links
-
-- [WiFi-Mat User Guide](docs/wifi-mat-user-guide.md) | [Domain Model](docs/ddd/wifi-mat-domain-model.md)
-- [ADR-021](docs/adr/ADR-021-vital-sign-detection-rvdna-pipeline.md) | [ADR-022](docs/adr/ADR-022-windows-wifi-enhanced-fidelity-ruvector.md) | [ADR-023](docs/adr/ADR-023-trained-densepose-model-ruvector-pipeline.md)
-
-
+- **Issue #11**: "AI Slop, no wifi reference in code"
+- **Issue #12**: "Vibe-coded non functional project with fake inflated stars"
+- **Issue #21**: "Non Functional AI Generated Repo with Inflated Stars - resume fraud"
+- **Issue #27**: "Has a single person tried running it? Even the author?"
+- **Issue #39**: "Purely fake repo.... Disappointed!"
---
-## 🧪 Testing
+## Warning
-
-542+ tests across 7 suites — zero mocks, hardware-free simulation
+If you found the original project through GitHub Trending or star counts, please be aware that the star count is highly suspected to be artificially inflated. The code cannot perform WiFi-based pose estimation in any capacity. **Do not rely on this for any research, product, or resume reference.**
-```bash
-# Rust tests (primary — 542+ tests)
-cd rust-port/wifi-densepose-rs
-cargo test --workspace
-
-# Sensing server tests (229 tests)
-cargo test -p wifi-densepose-sensing-server
-
-# Vital sign benchmark
-./target/release/sensing-server --benchmark
-
-# Python tests
-python -m pytest v1/tests/ -v
-
-# Pipeline verification (no hardware needed)
-./verify
-```
-
-| Suite | Tests | What It Covers |
-|-------|-------|----------------|
-| sensing-server lib | 147 | Graph transformer, trainer, SONA, sparse inference, RVF |
-| sensing-server bin | 48 | CLI integration, WebSocket, REST API |
-| RVF integration | 16 | Container build, read, progressive load |
-| Vital signs integration | 18 | FFT detection, breathing, heartbeat |
-| wifi-densepose-signal | 83 | SOTA algorithms, Doppler, Fresnel |
-| wifi-densepose-mat | 139 | Disaster response, triage, localization |
-| wifi-densepose-wifiscan | 91 | 8-stage RSSI pipeline |
-
-
+If you are genuinely interested in WiFi-based human sensing, refer to the actual CMU research:
+- Paper: [DensePose From WiFi](https://arxiv.org/abs/2301.00250)
+- Authors: Jiaqi Geng, Dong Huang, Fernando De la Torre (Carnegie Mellon University)
---
-## 🚀 Deployment
-
-
-Docker deployment — Production setup with docker-compose
-
-```bash
-# Rust sensing server (132 MB)
-docker pull ruvnet/wifi-densepose:latest
-docker run -p 3000:3000 -p 3001:3001 -p 5005:5005/udp ruvnet/wifi-densepose:latest
-
-# Python pipeline (569 MB)
-docker pull ruvnet/wifi-densepose:python
-docker run -p 8765:8765 -p 8080:8080 ruvnet/wifi-densepose:python
-
-# Both via docker-compose
-cd docker && docker compose up
-
-# Export RVF model
-docker run --rm -v $(pwd):/out ruvnet/wifi-densepose:latest --export-rvf /out/model.rvf
-```
-
-### Environment Variables
-
-```bash
-RUST_LOG=info # Logging level
-WIFI_INTERFACE=wlan0 # WiFi interface for RSSI
-POSE_CONFIDENCE_THRESHOLD=0.7 # Minimum confidence
-POSE_MAX_PERSONS=10 # Max tracked individuals
-```
-
-
-
----
-
-## 📊 Performance Metrics
-
-
-Measured benchmarks — Rust sensing server, validated via cargo bench
-
-### Rust Sensing Server
-
-| Metric | Value |
-|--------|-------|
-| Vital sign detection | **11,665 fps** (86 µs/frame) |
-| Full CSI pipeline | **54,000 fps** (18.47 µs/frame) |
-| Motion detection | **186 ns** (~5,400x vs Python) |
-| Docker image | 132 MB |
-| Memory usage | ~100 MB |
-| Test count | 542+ |
-
-### Python vs Rust
-
-| Operation | Python | Rust | Speedup |
-|-----------|--------|------|---------|
-| CSI Preprocessing | ~5 ms | 5.19 µs | 1000x |
-| Phase Sanitization | ~3 ms | 3.84 µs | 780x |
-| Feature Extraction | ~8 ms | 9.03 µs | 890x |
-| Motion Detection | ~1 ms | 186 ns | 5400x |
-| **Full Pipeline** | ~15 ms | 18.47 µs | **810x** |
-
-
-
----
-
-## 🤝 Contributing
-
-
-Dev setup, code standards, PR process
-
-```bash
-git clone https://github.com/ruvnet/wifi-densepose.git
-cd wifi-densepose
-
-# Rust development
-cd rust-port/wifi-densepose-rs
-cargo build --release
-cargo test --workspace
-
-# Python development
-python -m venv venv && source venv/bin/activate
-pip install -r requirements-dev.txt && pip install -e .
-pre-commit install
-```
-
-1. **Fork** the repository
-2. **Create** a feature branch (`git checkout -b feature/amazing-feature`)
-3. **Commit** your changes
-4. **Push** and open a Pull Request
-
-
-
----
-
-## 📄 Changelog
-
-
-Release history
-
-### v2.3.0 — 2026-03-01
-
-The largest release to date — delivers the complete end-to-end training pipeline, Docker images, and vital sign detection. The Rust sensing server now supports full model training, RVF export, and progressive model loading from a single binary.
-
-- **Docker images published** — `ruvnet/wifi-densepose:latest` (132 MB Rust) and `:python` (569 MB)
-- **8-phase DensePose training pipeline (ADR-023)** — Dataset loaders (MM-Fi, Wi-Pose), graph transformer with cross-attention, 6-term composite loss, cosine-scheduled SGD, PCK/OKS validation, SONA adaptation, sparse inference engine, RVF model packaging
-- **`--export-rvf` CLI flag** — Standalone RVF model container generation with vital config, training proof, and SONA profiles
-- **`--train` CLI flag** — Full training mode with best-epoch snapshotting and checkpoint saving
-- **Vital sign detection (ADR-021)** — FFT-based breathing (6-30 BPM) and heartbeat (40-120 BPM) extraction, 11,665 fps benchmark
-- **WiFi scan domain layer (ADR-022)** — 8-stage pure-Rust signal intelligence pipeline for Windows WiFi RSSI
-- **New crates** — `wifi-densepose-vitals` (1,863 lines) and `wifi-densepose-wifiscan` (4,829 lines)
-- **542+ Rust tests** — All passing, zero mocks
-
-### v2.2.0 — 2026-02-28
-
-Introduced the guided installer, SOTA signal processing algorithms, and the WiFi-Mat disaster response module. This release established the ESP32 hardware path and security hardening.
-
-- **Guided installer** — `./install.sh` with 7-step hardware detection and 8 install profiles
-- **6 SOTA signal algorithms (ADR-014)** — SpotFi conjugate multiplication, Hampel filter, Fresnel zone model, CSI spectrogram, subcarrier selection, body velocity profile
-- **WiFi-Mat disaster response** — START triage, scan zones, 3D localization, priority alerts — 139 tests
-- **ESP32 CSI hardware parser** — Binary frame parsing with I/Q extraction — 28 tests
-- **Security hardening** — 10 vulnerabilities fixed (CVE remediation, input validation, path security)
-
-### v2.1.0 — 2026-02-28
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-The foundational Rust release — ported the Python v1 pipeline to Rust with 810x speedup, integrated the RuVector signal intelligence crates, and added the Three.js real-time visualization.
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-- **RuVector integration** — 11 vendored crates (ADR-002 through ADR-013) for HNSW indexing, attention, GNN, temporal compression, min-cut, solver
-- **ESP32 CSI sensor mesh** — $54 starter kit with 3-6 ESP32-S3 nodes streaming at 20 Hz
-- **Three.js visualization** — 3D body model with 17 joints, real-time WebSocket streaming
-- **CI verification pipeline** — Determinism checks and unseeded random scan across all signal operations
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-
-
----
-
-## 📄 License
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-MIT License — see [LICENSE](LICENSE) for details.
-
-## 📞 Support
-
-[GitHub Issues](https://github.com/ruvnet/wifi-densepose/issues) | [Discussions](https://github.com/ruvnet/wifi-densepose/discussions) | [PyPI](https://pypi.org/project/wifi-densepose/)
-
----
-
-**WiFi DensePose** — Privacy-preserving human pose estimation through WiFi signals.
+*This fork is maintained as a public service to the developer community. All claims above are verifiable by reading the source code at the cited file paths and line numbers.*