wifi-densepose/rust-port/wifi-densepose-rs/crates

WiFi-DensePose Rust Crates

See through walls with WiFi. No cameras. No wearables. Just radio waves.

A modular Rust workspace for WiFi-based human pose estimation, vital sign monitoring, and disaster response using Channel State Information (CSI). Built on RuVector graph algorithms and the WiFi-DensePose research platform by rUv.

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Performance

Operation	Python v1	Rust v2	Speedup
CSI Preprocessing	~5 ms	5.19 us	~1000x
Phase Sanitization	~3 ms	3.84 us	~780x
Feature Extraction	~8 ms	9.03 us	~890x
Motion Detection	~1 ms	186 ns	~5400x
Full Pipeline	~15 ms	18.47 us	~810x
Vital Signs	N/A	86 us (11,665 fps)	--

Crate Overview

Core Foundation

Crate	Description	crates.io
wifi-densepose-core	Types, traits, and utilities (CsiFrame, PoseEstimate, SignalProcessor)
wifi-densepose-config	Configuration management (env, TOML, YAML)
wifi-densepose-db	Database persistence (PostgreSQL, SQLite, Redis)

Signal Processing & Sensing

Crate	Description	RuVector Integration	crates.io
wifi-densepose-signal	SOTA CSI signal processing (6 algorithms from SpotFi, FarSense, Widar 3.0)	ruvector-mincut, ruvector-attn-mincut, ruvector-attention, ruvector-solver
wifi-densepose-vitals	Vital sign extraction: breathing (6-30 BPM) and heart rate (40-120 BPM)	--
wifi-densepose-wifiscan	Multi-BSSID WiFi scanning for Windows-enhanced sensing	--

Neural Network & Training

Crate	Description	RuVector Integration	crates.io
wifi-densepose-nn	Multi-backend inference (ONNX, PyTorch, Candle) with DensePose head (24 body parts)	--
wifi-densepose-train	Training pipeline with MM-Fi dataset, 114->56 subcarrier interpolation	All 5 crates

Disaster Response

Crate	Description	RuVector Integration	crates.io
wifi-densepose-mat	Mass Casualty Assessment Tool -- survivor detection, triage, multi-AP localization	ruvector-solver, ruvector-temporal-tensor

Hardware & Deployment

Crate	Description	crates.io
wifi-densepose-hardware	ESP32, Intel 5300, Atheros CSI sensor interfaces (pure Rust, no FFI)
wifi-densepose-wasm	WebAssembly bindings for browser-based disaster dashboard
wifi-densepose-sensing-server	Axum server: ESP32 UDP ingestion, WebSocket broadcast, sensing UI

Applications

Crate	Description	crates.io
wifi-densepose-api	REST + WebSocket API layer
wifi-densepose-cli	Command-line tool for MAT disaster scanning

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Architecture

                          wifi-densepose-core
                         (types, traits, errors)
                                  |
              +-------------------+-------------------+
              |                   |                   |
    wifi-densepose-signal   wifi-densepose-nn   wifi-densepose-hardware
    (CSI processing)        (inference)         (ESP32, Intel 5300)
    + ruvector-mincut       + ONNX Runtime          |
    + ruvector-attn-mincut  + PyTorch (tch)   wifi-densepose-vitals
    + ruvector-attention    + Candle          (breathing, heart rate)
    + ruvector-solver            |
              |                  |             wifi-densepose-wifiscan
              +--------+---------+            (BSSID scanning)
                       |
          +------------+------------+
          |                         |
  wifi-densepose-train    wifi-densepose-mat
  (training pipeline)     (disaster response)
  + ALL 5 ruvector        + ruvector-solver
                          + ruvector-temporal-tensor
                                |
              +-----------------+-----------------+
              |                 |                 |
    wifi-densepose-api  wifi-densepose-wasm  wifi-densepose-cli
    (REST/WS)           (browser WASM)       (CLI tool)
              |
    wifi-densepose-sensing-server
    (Axum + WebSocket)

RuVector Integration

All RuVector crates at v2.0.4 from crates.io:

RuVector Crate	Used In	Purpose
ruvector-mincut	signal, train	Dynamic min-cut for subcarrier selection & person matching
ruvector-attn-mincut	signal, train	Attention-weighted min-cut for antenna gating & spectrograms
ruvector-temporal-tensor	train, mat	Tiered temporal compression (4-10x memory reduction)
ruvector-solver	signal, train, mat	Sparse Neumann solver for interpolation & triangulation
ruvector-attention	signal, train	Scaled dot-product attention for spatial features & BVP

Signal Processing Algorithms

Six state-of-the-art algorithms implemented in wifi-densepose-signal:

Algorithm	Paper	Year	Module
Conjugate Multiplication	SpotFi (SIGCOMM)	2015	csi_ratio.rs
Hampel Filter	WiGest	2015	hampel.rs
Fresnel Zone Model	FarSense (MobiCom)	2019	fresnel.rs
CSI Spectrogram	Standard STFT	2018+	spectrogram.rs
Subcarrier Selection	WiDance (MobiCom)	2017	subcarrier_selection.rs
Body Velocity Profile	Widar 3.0 (MobiSys)	2019	bvp.rs

Quick Start

As a Library

use wifi_densepose_core::{CsiFrame, CsiMetadata, SignalProcessor};
use wifi_densepose_signal::{CsiProcessor, CsiProcessorConfig};

// Configure the CSI processor
let config = CsiProcessorConfig::default();
let processor = CsiProcessor::new(config);

// Process a CSI frame
let frame = CsiFrame { /* ... */ };
let processed = processor.process(&frame)?;

Vital Sign Monitoring

use wifi_densepose_vitals::{
    CsiVitalPreprocessor, BreathingExtractor, HeartRateExtractor,
    VitalAnomalyDetector,
};

let mut preprocessor = CsiVitalPreprocessor::new(56); // 56 subcarriers
let mut breathing = BreathingExtractor::new(100.0);    // 100 Hz sample rate
let mut heartrate = HeartRateExtractor::new(100.0);

// Feed CSI frames and extract vitals
for frame in csi_stream {
    let residuals = preprocessor.update(&frame.amplitudes);
    if let Some(bpm) = breathing.push_residuals(&residuals) {
        println!("Breathing: {:.1} BPM", bpm);
    }
}

Disaster Response (MAT)

use wifi_densepose_mat::{DisasterResponse, DisasterConfig, DisasterType};

let config = DisasterConfig {
    disaster_type: DisasterType::Earthquake,
    max_scan_zones: 16,
    ..Default::default()
};

let mut responder = DisasterResponse::new(config);
responder.add_scan_zone(zone)?;
responder.start_continuous_scan().await?;

Hardware (ESP32)

use wifi_densepose_hardware::{Esp32CsiParser, CsiFrame};

let parser = Esp32CsiParser::new();
let raw_bytes: &[u8] = /* UDP packet from ESP32 */;
let frame: CsiFrame = parser.parse(raw_bytes)?;
println!("RSSI: {} dBm, {} subcarriers", frame.metadata.rssi, frame.subcarriers.len());

Training

# Check training crate (no GPU needed)
cargo check -p wifi-densepose-train --no-default-features

# Run training with GPU (requires tch/libtorch)
cargo run -p wifi-densepose-train --features tch-backend --bin train -- \
    --config training.toml --dataset /path/to/mmfi

# Verify deterministic training proof
cargo run -p wifi-densepose-train --features tch-backend --bin verify-training

Building

# Clone the repository
git clone https://github.com/ruvnet/wifi-densepose.git
cd wifi-densepose/rust-port/wifi-densepose-rs

# Check workspace (no GPU dependencies)
cargo check --workspace --no-default-features

# Run all tests
cargo test --workspace --no-default-features

# Build release
cargo build --release --workspace

Feature Flags

Crate	Feature	Description
wifi-densepose-nn	onnx (default)	ONNX Runtime backend
wifi-densepose-nn	tch-backend	PyTorch (libtorch) backend
wifi-densepose-nn	candle-backend	Candle (pure Rust) backend
wifi-densepose-nn	cuda	CUDA GPU acceleration
wifi-densepose-train	tch-backend	Enable GPU training modules
wifi-densepose-mat	ruvector (default)	RuVector graph algorithms
wifi-densepose-mat	api (default)	REST + WebSocket API
wifi-densepose-mat	distributed	Multi-node coordination
wifi-densepose-mat	drone	Drone-mounted scanning
wifi-densepose-hardware	esp32	ESP32 protocol support
wifi-densepose-hardware	intel5300	Intel 5300 CSI Tool
wifi-densepose-hardware	linux-wifi	Linux commodity WiFi
wifi-densepose-wifiscan	wlanapi	Windows WLAN API async scanning
wifi-densepose-core	serde	Serialization support
wifi-densepose-core	async	Async trait support

Testing

# Unit tests (all crates)
cargo test --workspace --no-default-features

# Signal processing benchmarks
cargo bench -p wifi-densepose-signal

# Training benchmarks
cargo bench -p wifi-densepose-train --no-default-features

# Detection benchmarks
cargo bench -p wifi-densepose-mat

Supported Hardware

Hardware	Crate Feature	CSI Subcarriers	Cost
ESP32-S3 Mesh (3-6 nodes)	hardware/esp32	52-56	~$54
Intel 5300 NIC	hardware/intel5300	30	~$50
Atheros AR9580	hardware/linux-wifi	56	~$100
Any WiFi (Windows/Linux)	wifiscan	RSSI-only	$0

Architecture Decision Records

Key design decisions documented in docs/adr/:

ADR	Title	Status
ADR-014	SOTA Signal Processing	Accepted
ADR-015	MM-Fi + Wi-Pose Training Datasets	Accepted
ADR-016	RuVector Training Pipeline	Accepted (Complete)
ADR-017	RuVector Signal + MAT Integration	Accepted
ADR-021	Vital Sign Detection Pipeline	Accepted
ADR-022	Windows WiFi Enhanced Sensing	Accepted
ADR-024	Contrastive CSI Embedding Model	Accepted

Related Projects

• WiFi-DensePose -- Main repository (Python v1 + Rust v2)
• RuVector -- Graph algorithms for neural networks (5 crates, v2.0.4)
• rUv -- Creator and maintainer

License

All crates are dual-licensed under MIT OR Apache-2.0.

Copyright (c) 2024 rUv