基于低成本物联网芯片ESP32的人体行为识别系统

doi:10.11959/j.issn.2096-3750.2023.00330

Abstract

Abstract:

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%.

Key words: human activity recognition, channel state information, KNN, discrete wavelet transform, dynamic time warping

CLC Number:

TP393

Chao HU, Bangyan LU, Yanbing YANG, Zhe CHEN, Lei ZHANG, Liangyin CHEN. Human activity recognition system based on low-cost IoT chip ESP32[J]. Chinese Journal on Internet of Things, 2023, 7(2): 133-142.

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References 30

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滤波器	平均识别准确率	处理时延/ms
Hampel滤波器	98.3%	2.1
中值滤波器	97.5%	20.4

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

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