Abstract: Gesture recognition has important application prospects in human-computer interaction.In recent years,with the rapid development of wireless communication and the Internet of Things,WiFi devices have been deployed almost anywhere,and a large number of gesture recognition methods have appeared on WiFi channel status information.At present,most researches based on CSI gesture recognition only focus on the research of gesture recognition in known domain.For unknown domain,new data in unknown scenes need to be added for additional learning and training,otherwise the recognition accuracy will be greatly reduced,limiting practicality.To address this problem,a CSI cross-domain gesture recognition method based on 3D convolutional neural network is proposed.The method realizes cross-scene gesture recognition by extracting domain-independent features,and combining with the 3D convolutional neural network learning model.In order to verify the method,experiment uses the public dataest.For 6 different gestures,the results show that the method achieves 86.50% recognition accuracy in known domain,and achieves 84.67% recognition accuracy in unknown scenes,it can achieve cross-scene gesture recognition.

Key words: 3D convolutional neural network, Channel state information, Cross-domain, Gesture recognition, WiFi