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.
Highlights
Benefiting from the widespread deployment of the wireless communication infrastructure, human behavior recognition based on wireless communication network technology has become a core technology to promote various applications [1, 2]
After the Butterworth low-pass filter filters out the abnormal XingYiQuan motion data before and after comparison, as shown in Figures 6(a)–6(d), it can be seen that the Channel State Information (CSI) data become smoother and high-frequency anomalies are filtered out
We propose a new complex human motion recognition method, namely, CSI-HC, which is verified by the XingYiQuan motion which is a complex motion. e core part is the denoising of CSI signals and the classification of complex motions
Summary
Benefiting from the widespread deployment of the wireless communication infrastructure, human behavior recognition based on wireless communication network technology has become a core technology to promote various applications [1, 2]. In order to recognize human behavior, physical sensing devices (e.g., ultra-wideband (UWB), radio frequency identification (RFID), and acceleration sensors) are first required to be worn on the human body or deployed in the environment. The existing WiFi-based human motion recognition scheme recognizes the action is relatively simple, the actual scene availability is not strong, or it is a daily behavior such as walking, opening the door, sleeping, or a single human body standing, picking up, and sitting down. (1) In this paper, a complex human motion recognition scheme CSI-HC based on WiFi is proposed and verified with the background of the Chinese traditional martial art XingYiQuan. Its advantage is that the detection motion is relatively complex and does not need human wearing equipment It can work effectively on cheap commercial devices and is of great value in guiding and monitoring human health campaigns.
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