基于低成本物联网芯片ESP32的人体行为识别系统

Human activity recognition system based on low-cost IoT chip ESP32

人体行为识别广泛存在于运动管理、行为分类等应用中,当前的人体行为识别应用主要分为基于摄像机、基于可穿戴设备和基于Wi-Fi感知3类。其中,基于摄像机的人体行为识别应用存在隐私泄露的风险,基于可穿戴设备的人体行为识别应用存在续航短、精度差等问题。基于Wi-Fi感知的人体行为识别一般通过Wi-Fi网卡或软件无线电设备识别信道状态信息变化的规律,从而推测用户行为,不存在隐私泄露和续航短的问题,但Wi-Fi网卡需要依靠计算机且软件无线电平台价格昂贵,极大地限制了Wi-Fi感知的应用场景。针对上述问题,提出了一种基于低成本物联网芯片ESP32的人体行为识别系统。具体地,所提系统首先使用Hampel滤波器和高斯滤波器对ESP32获得的信道状态信息进行预处理,然后使用主成分分析和离散小波变换降低数据的维度,最后通过K最近邻(KNN,K-nearest neighbor)算法对数据进行分类。实验结果表明该系统在仅使用两个ESP32节点的情况下,可以达到与当前主流Wi-Fi感知系统(Intel 5300网卡)相近的识别准确率,6种行为的平均准确率为98.6%。

Human activity recognition widely exists in applications such as sports management and activity classification.The current human activity recognition applications are mainly divided into three types:camera-based,wearable device-based,and Wi-Fi awareness-based.Among them,the camera-based human activity recognition application has the risk of privacy leakage,and the wearable device-based human activity recognition application has problems such as short battery life and poor accuracy.Human activity recognition based on Wi-Fi sensing generally uses Wi-Fi network cards or software-defined radio devices to identify the rules of channel state information changes,so as to infer user activity.It does not have the problems of privacy leakage and short battery life.But Wi-Fi network cards need to rely on computers and software-defined radio platforms are expensive,which greatly limit the application scenarios of Wi-Fi sensing.Aiming at the above problems,a human activity recognition system based on the low-cost IoT chip ESP32 was proposed.Specifically,the Hampel filter and Gaussian filter were used to preprocess the channel state information obtained by ESP32.Then,the principal component analysis and discrete wavelet transform were utilized to reduce the dimension of the data.Finally,the K-nearest neighbor(KNN)algorithm was applied to classify data.The experimental results show that the system can achieve a recognition accuracy which close to the current mainstream Wi-Fi perception system(Intel 5300 network card)when only two ESP32 nodes are deployed,and the average accuracy rate for the six activities is 98.6%.