Year: 2024
Type: conference-paper
Abstract: WiFi Channel State Information (CSI)-based Human Activity Recognition (HAR) enables contactless, long-range sensing in spatially constrained environments while preserving visual privacy. However, despite the ubiquity of WiFi-enabled devices, few expose CSI, limiting sensing hardware options. Variants of the Espressif ESP32 have emerged as potential compact, low-cost, and easy-to-deploy solutions for WiFi CSI-based HAR. In this work, four ESP32-S3-based 2.4 GHz directional antenna systems are evaluated for their ability to facilitate long-range through-wall HAR. Two promising systems are identified: one combines ESP32-S3 with a directional biquad antenna, and the second uses the built-in printed inverted-F antenna (PIFA) achieving directionality through a plane reflector. In a comprehensive evaluation of line-of-sight (LOS) and non-line-of-sight (NLOS) HAR performance, both systems are deployed in an office environment spanning a distance of 18 meters across five rooms. In this experimental setup, the Wallhack 1.8 k dataset, comprising 1,806 CSI amplitude spectrograms of human activities, is collected and made publicly available. Based on Wallhack1.8k, activity recogn tion models using the EfficientNetV2 architecture are trained to assess system performance in LOS and NLOS scenarios. For the core NLOS activity recognition problem, the biquad antenna and PIFA-based systems achieve accuracies of 92.0 ± 3.5 and 86.8 ± 4.7, respectively, demonstrating the feasibility of long-range through-wall HAR.
Authors: Julian Strohmayer, Martin Kampel
Institution: TU Wien
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4402917107