CSI-HC: A WiFi-Based Indoor Complex Human Motion Recognition Method

Abstract

WiFi indoor personnel behavior recognition has become the core technology of wireless network perception. However, the existing human behavior recognition methods have great challenges in terms of detection accuracy, intrusion, and complexity of operations. In this paper, we firstly analyze and summarize the existing human motion recognition schemes, and due to the existence of the problems in them, we propose a noninvasive, highly robust complex human motion recognition scheme based on Channel State Information (CSI), that is, CSI-HC, and the traditional Chinese martial art XingYiQuan is verified as a complex motion background. CSI-HC is divided into two phases: offline and online. In the offline phase, the human motion data are collected on the commercial Atheros NIC and a powerful denoising method is constructed by using the Butterworth low-pass filter and wavelet function to filter the outliers in the motion data. Then, through Restricted Boltzmann Machine (RBM) training and classification, we establish offline fingerprint information. In the online phase, SoftMax regression is used to correct the RBM classification to process the motion data collected in real time and the processed real-time data are matched with the offline fingerprint information. On this basis, the recognition of a complex human motion is realized. Finally, through repeated experiments in three classical indoor scenes, the parameter setting and user diversity affecting the accuracy of motion recognition are analyzed and the robustness of CSI-HC is detected. In addition, the performance of the proposed method is compared with that of the existing motion recognition methods. The experimental results show that the average motion recognition rate of CSI-HC in three classic indoor scenes reaches 85.4%, in terms of motion complexity and indoor recognition accuracy. Compared with other algorithms, it has higher stability and robustness.

WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals

“…At the same time, in order to further analyze the robustness of WiPg, we also compared WiPg with two other action-recognition methods (CSI-HC [ 15 ] and ABLSTM [ 16 ]). Similarly, we selected the three experimental environments mentioned above and 14 yoga action data sets collected by us for this comparative experiment.…”

“…In a typical study of indoor behavior recognition based on Wi-Fi CSI, Hao et al proposed a complex human-motion recognition scheme based on CSI [ 15 ], which is combined with a restricted Boltzmann machine (RBM) and SoftMax regression correction RBM classification. This method can classify the motion data sets effectively.…”

A Framework for Human Activity Recognition Based on WiFi CSI Signal Enhancement

“…In previous work, filter, outlier elimination, and interpolation are often used for data preprocessing, such as Butterworth filter [32], Kalman filter [33], Hampel filter, and discrete wavelet transform (DWT) [34]. However, these methods only reduced the noise instead of enhancing activity signals.…”

Wi-CAS: A Contactless Method for Continuous Indoor Human Activity Sensing Using Wi-Fi Devices

“…To compensate for the limitations of traditional technologies, human activity sensing using Wi-Fi signals has gradually gained attention. In recent years, related research based on Wi-Fi signals has also made great progress in action recognition [5], gesture recognition [6], fall detection [7], and person identification [8]. In previous studies, researchers have used Received Signal Strength (RSS) for human action recognition, such as the Wi-Gest [9] system to classify and recognize gestures by extracting the time-frequency domain features of RSS.…”