STAR: A Privacy-Preserving, Energy-Efficient Edge AI Framework for Human Activity Recognition via Wi-Fi CSI in Mobile and Pervasive Computing Environments

Human Activity Recognition (HAR) via Wi - Fi Channel State Information (CSI) presents a privacy - preserving, contactless sensing approach suitable for smart homes, healthcare monitoring, and mobile IoT systems. However, existing methods often encounter comput ational inefficiency, high latency, and limited feasibility within resource - constrained, embedded mobile edge environments. This paper proposes STAR (Sensing Technology for Activity Recognition), an edge - AI - optimized framework that integrates a lightweight neural architecture, adaptive signal processing, and hardware - aware co - optimization to enable real - time, energy - efficient HAR on low - power embedded devices. STAR incorporates a streamlined Gated Recurrent Unit (GRU) - based recurrent neural netwo rk, reducing model parameters by 33% compared to conventional LSTM models while maintaining effective temporal modeling capability. A multi - stage pre - processing pipeline combining median filtering, 8th - order Butterworth low - pass filtering, and Empirical Mo de Decomposition (EMD) is employed to denoise CSI amplitude data and extract spatial - temporal features. For on - device deployment, STAR is implemented on a Rockchip RV1126 processor equipped with an embedded Neural Processing Unit (NPU), interfaced with an ESP32 - S3 - based CSI acquisition module. Experimental results demonstrate a mean recognition accuracy of 93.52% across seven activity classes and 99.11% for human presence detection, utilizing a compact 97.6k - parameter model. INT8 quantized inference achieve s a processing speed of 33 MHz with just 8% CPU utilization, delivering sixfold speed improvements over CPU - based execution. With sub - second response latency and low power consumption, the system ensures real - time, privacy - preserving HAR, offering a practi cal, scalable solution for mobile and pervasive computing environments.