Why Commodity WiFi Sensors Fail at Multi-Person Gait Identification: A Systematic Analysis Using ESP32

TL;DR

This study systematically evaluates WiFi-based multi-person gait identification using commodity ESP32 sensors, revealing that hardware limitations cause poor performance rather than algorithmic issues.

Contribution

It provides the first comprehensive analysis showing hardware constraints fundamentally limit multi-person gait identification accuracy with low-cost WiFi sensors.

Findings

All tested methods achieved low accuracy (45-56%) with no significant differences.

High intra-subject variability and low inter-subject distinguishability were observed.

Performance degrades severely as the number of people increases.

Abstract

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\%, $\sigma$=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.