Research on Wi-Fi gesture recognition system based on DSC-SGRU model
Wi-Fi wireless sensing technology has become a research hotspot in the field of perception,which can realize intelligent perception of human activities and the surrounding environment.The existing wireless sensing models have a large number of parameters,which makes it difficult to sense in real-time in scenarios with limited computing power such as mobile devices.To this end,a classification and recognition model based on a mixture of a lightweight feature extraction module based on depth-separable convolution and a stacked gated recurrent unit is proposed.Firstly,a lightweight feature extraction module based on depth-separable convolution is constructed to capture the spatial features of human gestures and keep the temporal nature of the features unchanged;then the spatio-temporal features of human gestures are learned using a two-layer stacked GRU network;finally,the performance of the model is validated using the open-source dataset Widar,and the BVP features in the CSI information are extracted to improve the recognition of cross-domain scenes accuracy,and a weighted loss function is utilized to solve the sample imbalance problem.The results show that the proposed model achieves an accuracy of 77.6%in cross-domain scenarios with a parameter count of only 236.891 K.Compared with other existing Wi-Fi gesture recognition models,the proposed model greatly reduces the parameters and computational complexity of the model while its performance remains basically unchanged,which lays a foundation for the popularization of the Wi-Fi wireless sensing technology in practical applications.
wireless sensingchannel state informationdeep learninggesture recognitioncross-domain
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