* feat(worldmodel): ADR-147 — OccWorld integration, wifi-densepose-worldmodel v0.3.0 (#854) - New crate `wifi-densepose-worldmodel` v0.3.0: async Unix-socket bridge to OccWorld Python inference server; `OccWorldBridge`, `OccupancyGrid3D`, `TrajectoryPrior`, `worldgraph_to_occupancy` encoder (14/14 tests pass) - `scripts/occworld_server.py`: long-lived Python inference server for OccWorld TransVQVAE (72.4M params); applies API-bug patches; dummy mode for CI testing; graceful SIGTERM shutdown - `pose_tracker.rs`: `trajectory_prior` soft-blend injection (80/20 Kalman/prior) on torso keypoint; `set_trajectory_prior()` public method - CI: added `Run ADR-147 worldmodel tests` step - ADR-147: accepted — OccWorld primary (209 ms, 3.37 GB VRAM, RTX 5080); Cosmos deferred to ADR-148 (32.54 GB VRAM exceeds hardware) - Benchmark proof: 208.7 ms P50, 3.37 GB peak VRAM, 12.1 GB headroom Co-Authored-By: claude-flow <ruv@ruv.net> * chore: update ruvector.db state Co-Authored-By: claude-flow <ruv@ruv.net> * chore: ruvector.db sync Co-Authored-By: claude-flow <ruv@ruv.net> * fix(cli): add missing min_frames field to CalibrateArgs test helper E0063 in calibrate.rs:448 — CalibrateArgs gained min_frames in ADR-135 but the default_args() test helper was not updated. min_frames=0 means 'use tier default', matching the existing runtime behaviour. 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