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 presence of numerous
WiFi-enabled devices around us, few expose CSI to users, resulting in a lack of
sensing hardware options. Variants of the Espressif ESP32 have emerged as
potential low-cost and easy-to-deploy solutions for WiFi CSI-based HAR. In this
work, four ESP32-S3-based 2.4GHz directional antenna systems are evaluated for
their ability to facilitate long-range through-wall HAR. Two promising systems
are proposed, one of which combines the ESP32-S3 with a directional biquad
antenna. This combination represents, to the best of our knowledge, the first
demonstration of such a system in WiFi-based HAR. The second system relies on
the built-in printed inverted-F antenna (PIFA) of the ESP32-S3 and achieves
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 Wallhack1.8k dataset, comprising
1806 CSI amplitude spectrograms of human activities, is collected and made
publicly available. Based on Wallhack1.8k, we train activity recognition models
using the EfficientNetV2 architecture 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$\pm$3.5 and
86.8$\pm$4.7, respectively, demonstrating the feasibility of long-range
through-wall HAR with the proposed systems.

基于 WiFi 信道状态信息（CSI）的无线人体活动识别（HAR）通过保护视觉隐私，在空间受限的环境中实现了无接触、远距离感知。本研究评估了基于 ESP32-S3 芯片的 4 种 2.4GHz 定向天线系统，以促进墙壁背后的远距离人体活动识别，提出了两种有前景的系统：一种是将 ESP32-S3 与定向双平截头天线结合，另一种则通过平面反射器实现方向性，并通过 ESP32-S3 的内置印刷倒 “F” 天线（PIFA）进行识别。在实验环境中，使用 Wallhack1.8k 数据集（包含 1806 个人体活动的 CSI 幅度谱图）对两种系统的直视和非直视条件下的人体活动识别性能进行综合评估，所提出的系统在非直视条件下的精度分别达到 92.0$pm$3.5 和 86.8$pm$4.7，证明了实现墙壁背后的长距离人体活动识别的可行性。