摘要： 现有人类行为识别方法识别精度低、成本高，所能识别的动作也相对简单。为此，通过引入信道状态信息（CSI）提出一种人体复杂动作识别方法，并以传统武术形意拳招式动作为背景进行验证。利用Wi-Fi网卡采集形意拳招式的CSI数据，以数据中的振幅为特征值，使用巴特沃斯低通滤波器和离散小波变换分别过滤数据中的高频和低频异常值。离线阶段采用受限波尔兹曼机对预处理数据进行训练和分类，并构建形意拳招式指纹库。在线阶段使用深度置信网络对采集数据进行分类，将分类结果与指纹库数据进行匹配，实现对形意拳招式的准确识别。实验结果表明，与CSI-SRC方法和基于传统RSSI模型的方法相比，该方法具有较高的识别精度，并且鲁棒性较好。

关键词: 信道状态信息, 巴特沃斯低通滤波器, 离散小波变换, 受限玻尔兹曼机, 人体复杂动作识别

Abstract: Most of the existing human motion recognition methods have drawbacks such as low recognition accuracy,high cost,and limited identification ability,in that only simple motions can be identified.Therefore,this paper proposes a human complex motion recognition method based on Channel State Information(CSI),which is verified by the actions of traditional martial arts Xing Yi Quan.First,the Wi-Fi network adapter is used to collect the CSI data of Xing Yi Quan.Then,the amplitude of the collected data is used as the characteristic value,and the high frequency and low frequency abnormal values are respectively filtered by the Butterworth low pass filter and the Discrete Wavelet Transform(DWT).In the offline phase,the Restricted Boltzmann Machine(RBM) is adopted for the training and classification of pre-processing data,thus building the fingerprint database of Xing Yi Quan.In the online phase,the Deep Belief Network(DBN) is applied for the classification of collected data,and the classification results are matched with the fingerprints database,so the accurate recognition of Xing Yi Quan actions is realized.Experimental results show that compared with CSI-SRC method and the traditional RSSI model method,the proposed method has better recognition accuracy and robustness.

Key words: Channel State Information(CSI), Butterworth low pass filter, Discrete Wavelet Transform(DWT), Restricted Boltzmann Machine(RBM), human complex motion recognition

中图分类号: 

• TP391