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

2026-01-05

arXiv.org

AI Summary

This study investigates the fundamental limitations of low-cost commercial Wi-Fi devices—specifically the ESP32 platform—in multi-person gait recognition. Through a systematic evaluation of six signal separation techniques (FastICA, SOBI, PCA, NMF, wavelet transform, and tensor decomposition) across scenarios involving 1 to 10 individuals, and by introducing a novel set of multidimensional diagnostic metrics, the work demonstrates for the first time that the primary bottleneck lies in the insufficient quality of channel state information (CSI) captured by the hardware, rather than in algorithmic shortcomings. Experimental results show that all methods achieve only 45–56% accuracy (σ = 3.74%), with performance degrading significantly as the number of subjects increases, thereby confirming that the ESP32 is ill-suited for reliable multi-person gait recognition.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• multi-person gait identification
• commodity WiFi sensors
• Channel State Information
• signal separation
• ESP32

Innovation

• WiFi CSI
• multi-person gait identification
• commodity sensors
• signal separation
• ESP32