948c966226
- WiFi CSI-based indoor positioning system for self-hosted home environments - docs/plan/plan.md: full 9-phase implementation plan (65 gaps closed by analysis) - docs/research/: CSI fundamentals, physics, algorithms, signal processing, mesh topology, accuracy limits, literature - docs/notes/: recovery mechanisms, simulation testing, UX visualization - .marathon/instruction.md: per-iteration marathon instructions with detailed commit format - .marathon/start.sh: GLM-5 tmux launcher via ZAI proxy Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
123 lines
6.3 KiB
Markdown
123 lines
6.3 KiB
Markdown
# CARM: CSI-Based Activity Recognition and Monitoring
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**Authors:** Wei Wang, Alex X. Liu, Muhammad Shahzad, Kang Ling, Sanglu Lu
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**Venue:** ACM MobiCom 2015
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**DOI:** [10.1145/2789168.2790093](https://doi.org/10.1145/2789168.2790093)
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**Institution:** Michigan State University / Nanjing University
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---
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## Citation
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```
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@inproceedings{wang2015carm,
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title = {Understanding and Modeling of WiFi Signal Based Human Activity Recognition},
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author = {Wang, Wei and Liu, Alex X. and Shahzad, Muhammad and Ling, Kang and Lu, Sanglu},
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booktitle = {Proceedings of the 21st Annual International Conference on Mobile Computing and Networking},
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series = {MobiCom '15},
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year = {2015},
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pages = {65--76},
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doi = {10.1145/2789168.2790093},
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publisher = {ACM}
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}
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```
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---
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## Abstract
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> "Some pioneer WiFi signal based human activity recognition systems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two theoretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mechanism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%."
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---
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## Problem Statement
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Pioneer systems (WiSee, E-eyes, WiHear) lack quantitative models correlating CSI measurements to human activities. Without a model, optimisation is trial-and-error; systems cannot generalise to new environments or users without retraining. CARM is the first system to provide a **closed-form theoretical model** deriving activity information from physical CSI dynamics.
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---
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## Core Theory
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### Why Phase is Unusable
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Raw CSI phase drifts up to 50π between consecutive frames due to Carrier Frequency Offset (CFO). CARM measurements show CFO of ~80 kHz, causing 8π phase shift per 50 µs — body movement signals are ≤0.5π and are entirely buried in noise.
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**Solution:** Use CSI power `|H(f,t)|²` instead of phase. Power is **invariant to CFO** because the CFO term `e^{−j2πΔft}` cancels out in the magnitude operation.
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### CSI-Speed Model
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For multipath channel with K dynamic paths, CFR power decomposes as:
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```
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|H(f,t)|² = Σ_{k∈P_d} 2|H_s(f)·a_k| · cos(2πv_k·t/λ + 2πd_k(0)/λ + φ_{sk})
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+ Σ_{k,l∈P_d, k≠l} 2|a_k·a_l| · cos(2π(v_k−v_l)t/λ + ...)
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+ Σ_{k∈P_d} |a_k|² + |H_s(f)|² (Eq. 3)
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```
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**Key insight:** CFR power = DC offset + sinusoids with frequencies `v_k/λ` (path length change speeds in units of wavelengths/sec). **Measuring the frequency of CSI power oscillation directly measures the speed of the reflecting body part.**
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At 5 GHz (λ = 5.15 cm), 300 Hz → 15.45 m/s — well above human movement speeds. The useful band is 0–80 Hz for indoor activities.
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### PCA-Based Denoising
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CSI streams across subcarriers are correlated time-varying signals with different initial phases (due to multipath path length differences). They share the same time-varying component (body motion) but differ in the static offset:
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```
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|H_k(f,t)|² ≈ A_k(t) + C_k (same time variation, different constant)
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```
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PCA extracts the first principal component (the shared time-varying signal) while suppressing:
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- Impulse/burst noise from rate adaptation (affects all subcarriers simultaneously but incoherently with body motion)
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- Frequency-selective static offsets
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### DWT Feature Extraction
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Discrete Wavelet Transform separates activity speed components:
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| Activity | Dominant frequency | Physical speed |
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|---|---|---|
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| Walking (torso) | 35–40 Hz | 0.9–1.0 m/s |
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| Walking (legs) | 50–70 Hz | 1.3–1.8 m/s |
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| Falling | 40–80 Hz (brief spike) | 1.0–2.0 m/s (acceleration) |
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| Sitting down | < 40 Hz | < 1.0 m/s |
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| Brushing teeth | < 1 Hz | < 0.025 m/s |
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### CSI-Activity Model (HMM)
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Each activity = sequence of speed states over time. Hidden Markov Model captures state transitions:
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- Fall: {slow → fast-up → sudden-silence}
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- Walk: {sustained 35–40 Hz}
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- Sit: {brief 20–30 Hz → silence}
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Training: DWT speed profile features from **780+ activity samples across 25 volunteers**. The model generalises across users and environments because it is grounded in the physics of body motion speeds, not environment-specific features.
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### Activity Detection
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In a static environment, the first PCA eigenvector varies randomly (incoherent). During activity, CSI streams become correlated → eigenvector becomes smooth. High-frequency energy of eigenvector vs. adaptive threshold → detect activity start/end boundaries.
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---
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## Results
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| Scenario | Accuracy |
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| Same environment and person (training match) | **>96%** |
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| New environment (generalisation) | **>80%** |
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| New person (generalisation) | **>80%** |
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Compared to RSSI-based: 56–72% accuracy. Compared to WiSee (USRP hardware): CARM matches at 96% using **commodity hardware**.
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---
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## Limitations
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- Single-person scenario; multi-person requires blind signal separation (noted as future work)
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- Person must be within range of a TX-RX link
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- Activities with very similar speed profiles may be confused
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- 2D movement assumed (height information not captured)
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- HMM training still required per activity class (though not per environment or user)
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---
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## Relevance to Spaxel
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CARM's CSI-speed model is the theoretical justification for why Spaxel's amplitude variance metric works: body movement at speed v produces CSI power oscillations at frequency v/λ. This directly justifies the motion-gated baseline update in Spaxel — when this oscillation frequency is below the stability threshold, the scene is genuinely quiet, not just slow-moving. CARM's PCA denoising approach is a practical technique for extracting the motion signal from multi-subcarrier CSI before feeding the baseline estimator.
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