Why Commodity WiFi Sensors Fail at Multi-Person Gait Identification: A Systematic Analysis Using ESP32
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914233965346816 |
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| author | Custance, Oliver Khan, Saad Parkinson, Simon |
| author_facet | Custance, Oliver Khan, Saad Parkinson, Simon |
| contents | WiFi Channel State Information (CSI) has shown promise for single-person gait identification, with numerous studies reporting high accuracy. However, multi-person identification remains largely unexplored, with the limited existing work relying on complex, expensive setups requiring modified firmware. A critical question remains unanswered: is poor multi-person performance an algorithmic limitation or a fundamental hardware constraint? We systematically evaluate six diverse signal separation methods (FastICA, SOBI, PCA, NMF, Wavelet, Tensor Decomposition) across seven scenarios with 1-10 people using commodity ESP32 WiFi sensors--a simple, low-cost, off-the-shelf solution. Through novel diagnostic metrics (intra-subject variability, inter-subject distinguishability, performance degradation rate), we reveal that all methods achieve similarly low accuracy (45-56\%, $σ$=3.74\%) with statistically insignificant differences (p $>$ 0.05). Even the best-performing method, NMF, achieves only 56\% accuracy. Our analysis reveals high intra-subject variability, low inter-subject distinguishability, and severe performance degradation as person count increases, indicating that commodity ESP32 sensors cannot provide sufficient signal quality for reliable multi-person separation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02177 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Why Commodity WiFi Sensors Fail at Multi-Person Gait Identification: A Systematic Analysis Using ESP32 Custance, Oliver Khan, Saad Parkinson, Simon Computer Vision and Pattern Recognition Cryptography and Security WiFi Channel State Information (CSI) has shown promise for single-person gait identification, with numerous studies reporting high accuracy. However, multi-person identification remains largely unexplored, with the limited existing work relying on complex, expensive setups requiring modified firmware. A critical question remains unanswered: is poor multi-person performance an algorithmic limitation or a fundamental hardware constraint? We systematically evaluate six diverse signal separation methods (FastICA, SOBI, PCA, NMF, Wavelet, Tensor Decomposition) across seven scenarios with 1-10 people using commodity ESP32 WiFi sensors--a simple, low-cost, off-the-shelf solution. Through novel diagnostic metrics (intra-subject variability, inter-subject distinguishability, performance degradation rate), we reveal that all methods achieve similarly low accuracy (45-56\%, $σ$=3.74\%) with statistically insignificant differences (p $>$ 0.05). Even the best-performing method, NMF, achieves only 56\% accuracy. Our analysis reveals high intra-subject variability, low inter-subject distinguishability, and severe performance degradation as person count increases, indicating that commodity ESP32 sensors cannot provide sufficient signal quality for reliable multi-person separation. |
| title | Why Commodity WiFi Sensors Fail at Multi-Person Gait Identification: A Systematic Analysis Using ESP32 |
| topic | Computer Vision and Pattern Recognition Cryptography and Security |
| url | https://arxiv.org/abs/2601.02177 |