WiFi CSI gesture recognition based on parallel LSTM-FCN deep space-time neural network

Abstract

In this study, we developed a system based on deep space-time neural networks for gesture recognition. When users change or the number of gesture categories increases, the accuracy of gesture recognition decreases considerably because most gesture recognition systems cannot accommodate both user differentiation and gesture diversity. To overcome the limitations of existing methods, we designed a one- dimensional parallel long short-term memory-fully convolutional network (LSTM-FCN) model to extract gesture features of different dimensions. LSTM can learn complex time dynamic information, whereas FCN can predict gestures efficiently by extracting the deep, abstract features of gestures in the spatial dimension. In the experiment, 50 types of gestures of five users were collected and evaluated. The experimental results demonstrate the effectiveness of this system and robustness to various gestures and individual changes. Statistical analysis of the recognition results indicated that an average accuracy of approximately 98.9% was achieved.