用Wi-Fi信号实现低功耗实时人体行为识别，兼顾隐私与效率。
STAR: A Privacy-Preserving, Energy-Efficient Edge AI Framework for Human Activity Recognition via Wi-Fi CSI in Mobile and Pervasive Computing Environments
- 轻量GRU模型减少33%参数，保持时间特征捕捉能力。
- 多阶段去噪提取时空特征，7类活动识别准确率达93.52%。
- 在嵌入式NPU上实现毫秒级响应，适合智能终端部署。
通过Wi-Fi信道状态信息（CSI）进行人体行为识别（HAR）是一种无需接触且保护隐私的传感方式，适用于智能家居、医疗监护和移动物联网系统。然而，现有方法常面临计算效率低、延迟高，在资源受限的嵌入式边缘环境难以落地的问题。本文提出STAR（Sensing Technology for Activity Recognition）框架，集成轻量化神经网络、自适应信号处理与硬件感知协同优化，实现低功耗嵌入式设备上的实时高效HAR。STAR采用简化版门控循环单元（GRU）结构，相较传统LSTM模型减少33%参数，仍具备良好时序建模能力；通过中值滤波、8阶巴特沃斯低通滤波及经验模态分解（EMD）构成多级预处理流程，有效去除CSI幅度数据噪声并提取时空特征。系统在搭载嵌入式NPU的Rockchip RV1126处理器上部署，配合ESP32-S3采集模块，实验表明：在7类活动上平均识别准确率为93.52%，人体存在检测准确率达99.11%，仅使用97.6k参数的紧凑模型。INT8量化推理达33 MHz处理速度，仅需8% CPU利用率，较纯CPU执行提升六倍。系统响应延迟低于一秒，功耗极低，满足移动与普适计算场景下实时、隐私保护的HAR需求。
原文摘要 · Abstract (English)
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 computational 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 network, 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 Mode 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 achieves 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 practical, scalable solution for mobile and pervasive computing environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。