0a30f7904d
Phase 1: HardwareNormalizer (hardware_norm.rs, 399 lines, 14 tests)
- Catmull-Rom cubic interpolation: any subcarrier count → canonical 56
- Z-score normalization, phase unwrap + linear detrend
- Hardware detection: ESP32-S3, Intel 5300, Atheros, Generic
Phase 2: DomainFactorizer + GRL (domain.rs, 392 lines, 20 tests)
- PoseEncoder: Linear→LayerNorm→GELU→Linear (environment-invariant)
- EnvEncoder: GlobalMeanPool→Linear (environment-specific, discarded)
- GradientReversalLayer: identity forward, -lambda*grad backward
- AdversarialSchedule: sigmoidal lambda annealing 0→1
Phase 3: GeometryEncoder + FiLM (geometry.rs, 364 lines, 14 tests)
- FourierPositionalEncoding: 3D coords → 64-dim
- DeepSets: permutation-invariant AP position aggregation
- FilmLayer: Feature-wise Linear Modulation for zero-shot deployment
Phase 4: VirtualDomainAugmentor (virtual_aug.rs, 297 lines, 10 tests)
- Room scale, reflection coeff, virtual scatterers, noise injection
- Deterministic Xorshift64 RNG, 4x effective training diversity
Phase 5: RapidAdaptation (rapid_adapt.rs, 255 lines, 7 tests)
- 10-second unsupervised calibration via contrastive TTT + entropy min
- LoRA weight generation without pose labels
Phase 6: CrossDomainEvaluator (eval.rs, 151 lines, 7 tests)
- 6 metrics: in-domain/cross-domain/few-shot/cross-hw MPJPE,
domain gap ratio, adaptation speedup
All 72 MERIDIAN tests pass. Full workspace compiles clean.
Co-Authored-By: claude-flow <ruv@ruv.net>
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