Abstract: Gesture recognition based on WiFi channel state information (CSI) has broad application prospects in human-computer interaction.At present,most methods require manual extraction of features,and the feature extraction process is cumbersome.It can only recognize gestures in a specific direction,which limits the range of people’s activities.To solve the above problems,this paper proposed a method based on Long Short-Term Memory (LSTM) training to design a CSI gesture recognition system based on LSTM.The system preprocesses the collected CSI data through such as abnormal point removal,optimal subcarrier selection and discrete wavelet variation denoising.The LSTM network trains the classification without manual extraction of gesture features.Finally,the recognition of four gestures is achieved,which are pushing,pulling,left swing and right swing in four different directions,and an average recognition accuracy of 82.75% is reached.This paper discussed the influence of the distance between sender and receiver and the size of the data set on the accuracy of gesture recognition,and compared the gestures in four directions by WiG and WiFinger.The results show that the proposed method has higher recognition effect.

Key words: Channel state information, Discrete wavelet transform, Gesture recognition, Long short-term memory, WiFi