7092f83b34
Add license, authors, repository, documentation, keywords, categories, and readme fields to all crate Cargo.toml files. Add crate-level README files for documentation. Co-Authored-By: claude-flow <ruv@ruv.net>
87 lines
3.4 KiB
Markdown
87 lines
3.4 KiB
Markdown
# wifi-densepose-signal
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[](https://crates.io/crates/wifi-densepose-signal)
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[](https://docs.rs/wifi-densepose-signal)
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[](LICENSE)
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State-of-the-art WiFi CSI signal processing for human pose estimation.
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## Overview
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`wifi-densepose-signal` implements six peer-reviewed signal processing algorithms that extract
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human motion features from raw WiFi Channel State Information (CSI). Each algorithm is traced
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back to its original publication and integrated with the
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[ruvector](https://crates.io/crates/ruvector-mincut) family of crates for high-performance
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graph and attention operations.
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## Algorithms
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| Algorithm | Module | Reference |
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|-----------|--------|-----------|
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| Conjugate Multiplication | `csi_ratio` | SpotFi, SIGCOMM 2015 |
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| Hampel Filter | `hampel` | WiGest, 2015 |
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| Fresnel Zone Model | `fresnel` | FarSense, MobiCom 2019 |
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| CSI Spectrogram | `spectrogram` | Common in WiFi sensing literature since 2018 |
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| Subcarrier Selection | `subcarrier_selection` | WiDance, MobiCom 2017 |
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| Body Velocity Profile (BVP) | `bvp` | Widar 3.0, MobiSys 2019 |
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## Features
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- **CSI preprocessing** -- Noise removal, windowing, normalization via `CsiProcessor`.
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- **Phase sanitization** -- Unwrapping, outlier removal, and smoothing via `PhaseSanitizer`.
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- **Feature extraction** -- Amplitude, phase, correlation, Doppler, and PSD features.
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- **Motion detection** -- Human presence detection with confidence scoring via `MotionDetector`.
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- **ruvector integration** -- Graph min-cut (person matching), attention mechanisms (antenna and
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spatial attention), and sparse solvers (subcarrier interpolation).
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## Quick Start
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```rust
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use wifi_densepose_signal::{
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CsiProcessor, CsiProcessorConfig,
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PhaseSanitizer, PhaseSanitizerConfig,
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MotionDetector,
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};
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// Configure and create a CSI processor
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let config = CsiProcessorConfig::builder()
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.sampling_rate(1000.0)
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.window_size(256)
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.overlap(0.5)
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.noise_threshold(-30.0)
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.build();
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let processor = CsiProcessor::new(config);
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```
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## Architecture
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```text
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wifi-densepose-signal/src/
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lib.rs -- Re-exports, SignalError, prelude
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bvp.rs -- Body Velocity Profile (Widar 3.0)
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csi_processor.rs -- Core preprocessing pipeline
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csi_ratio.rs -- Conjugate multiplication (SpotFi)
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features.rs -- Amplitude/phase/Doppler/PSD feature extraction
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fresnel.rs -- Fresnel zone diffraction model
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hampel.rs -- Hampel outlier filter
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motion.rs -- Motion and human presence detection
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phase_sanitizer.rs -- Phase unwrapping and sanitization
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spectrogram.rs -- Time-frequency CSI spectrograms
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subcarrier_selection.rs -- Variance-based subcarrier selection
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```
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## Related Crates
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| Crate | Role |
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|-------|------|
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| [`wifi-densepose-core`](../wifi-densepose-core) | Foundation types and traits |
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| [`ruvector-mincut`](https://crates.io/crates/ruvector-mincut) | Graph min-cut for person matching |
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| [`ruvector-attn-mincut`](https://crates.io/crates/ruvector-attn-mincut) | Attention-weighted min-cut |
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| [`ruvector-attention`](https://crates.io/crates/ruvector-attention) | Spatial attention for CSI |
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| [`ruvector-solver`](https://crates.io/crates/ruvector-solver) | Sparse interpolation solver |
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## License
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MIT OR Apache-2.0
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