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* chore: update vendored ruvector to latest main (v2.1.0-40) Was at v2.0.5-172 (f8f2c600a), now at v2.1.0-40 (050c3fe6f). 316 commits with new crates: ruvector-coherence, sona, ruvector-core, ruvector-gnn improvements, and security hardening. Co-Authored-By: claude-flow <ruv@ruv.net> * feat: RuVector Phases 2+3 — temporal smoothing, kinematic constraints, coherence gating Phase 2 (sensing server): - Temporal keypoint smoothing via EMA (alpha=0.3) with coherence-adaptive blending - Coherence scoring: running variance of motion_energy over 20 frames - Low coherence → reduce alpha to 0.1 (trust measurements less) - Per-node prev_keypoints for frame-to-frame smoothing - Bone length clamping (±20%) in derive_single_person_pose Phase 3 (signal crate): - SkeletonConstraints: Jakobsen relaxation (3 iterations) on 12-bone COCO-17 kinematic tree — prevents impossible skeletons - CompressedPoseHistory: two-tier storage (hot f32 + warm i16 quantized) for trajectory matching and re-ID - 8 new tests for constraints + history Vendored ruvector updated to v2.1.0-40 (latest main, 316 commits). Workspace deps remain at v2.0.4 (crates.io) until v2.1.0 is published. 647 tests pass across both crates (0 failures). Refs #296 Co-Authored-By: claude-flow <ruv@ruv.net> |
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README.md
wifi-densepose-signal
State-of-the-art WiFi CSI signal processing for human pose estimation.
Overview
wifi-densepose-signal implements six peer-reviewed signal processing algorithms that extract
human motion features from raw WiFi Channel State Information (CSI). Each algorithm is traced
back to its original publication and integrated with the
ruvector family of crates for high-performance
graph and attention operations.
Algorithms
| Algorithm | Module | Reference |
|---|---|---|
| Conjugate Multiplication | csi_ratio |
SpotFi, SIGCOMM 2015 |
| Hampel Filter | hampel |
WiGest, 2015 |
| Fresnel Zone Model | fresnel |
FarSense, MobiCom 2019 |
| CSI Spectrogram | spectrogram |
Common in WiFi sensing literature since 2018 |
| Subcarrier Selection | subcarrier_selection |
WiDance, MobiCom 2017 |
| Body Velocity Profile (BVP) | bvp |
Widar 3.0, MobiSys 2019 |
Features
- CSI preprocessing -- Noise removal, windowing, normalization via
CsiProcessor. - Phase sanitization -- Unwrapping, outlier removal, and smoothing via
PhaseSanitizer. - Feature extraction -- Amplitude, phase, correlation, Doppler, and PSD features.
- Motion detection -- Human presence detection with confidence scoring via
MotionDetector. - ruvector integration -- Graph min-cut (person matching), attention mechanisms (antenna and spatial attention), and sparse solvers (subcarrier interpolation).
Quick Start
use wifi_densepose_signal::{
CsiProcessor, CsiProcessorConfig,
PhaseSanitizer, PhaseSanitizerConfig,
MotionDetector,
};
// Configure and create a CSI processor
let config = CsiProcessorConfig::builder()
.sampling_rate(1000.0)
.window_size(256)
.overlap(0.5)
.noise_threshold(-30.0)
.build();
let processor = CsiProcessor::new(config);
Architecture
wifi-densepose-signal/src/
lib.rs -- Re-exports, SignalError, prelude
bvp.rs -- Body Velocity Profile (Widar 3.0)
csi_processor.rs -- Core preprocessing pipeline
csi_ratio.rs -- Conjugate multiplication (SpotFi)
features.rs -- Amplitude/phase/Doppler/PSD feature extraction
fresnel.rs -- Fresnel zone diffraction model
hampel.rs -- Hampel outlier filter
motion.rs -- Motion and human presence detection
phase_sanitizer.rs -- Phase unwrapping and sanitization
spectrogram.rs -- Time-frequency CSI spectrograms
subcarrier_selection.rs -- Variance-based subcarrier selection
Related Crates
| Crate | Role |
|---|---|
wifi-densepose-core |
Foundation types and traits |
ruvector-mincut |
Graph min-cut for person matching |
ruvector-attn-mincut |
Attention-weighted min-cut |
ruvector-attention |
Spatial attention for CSI |
ruvector-solver |
Sparse interpolation solver |
License
MIT OR Apache-2.0