Published 2023-12-01
“…Human activity sensing based on Wi-Fi channel state information (CSI) has an important application prospect in future intelligent interaction scenarios such as virtual reality, intelligent
games, and the metaverse.Accurate sensing of complex and continuous human activities is an important challenge for Wi-Fi sensing.Convolutional neural network (CNN) has the ability of spatial feature extraction but is poor at modeling the temporal features of the data.While long short-term memory (LSTM) network or gated recurrent
unit (GRU) network, which are suitable for modeling time-series data, neglect learning spatial features of data.In order to solve this problem, an improved CNN that integrates bidirectional gated recurrent
unit (BiGRU) network was proposed.The bi-directional feature extraction ability of BiGRU was used to capture the correlation and dependence of the
front and back information in the time series data.The extraction of the spatiotemporal features of the time series CSI data was realized, and then the mapping relationship between the action and the CSI data was present.Thus the recognition accuracy of the complex continuous action was improved.The proposed network structure was tested with basketball actions.The results show that the recognition accuracy of this method is above 95% under various conditions.Compared with the traditional multi-layer perceptron (MLP), CNN, LSTM, GRU, and attention based bidirectional long short-term memory (ABLSTM) baseline methods, the recognition accuracy has been improved by 1%～20%.…”
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