WiFi CSI Gesture Recognition Based on Parallel LSTM-FCN Deep Space-Time Neural Network

• 来源期刊: 中国通信（英文版）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: SIGNAL PROCESSING
• 页码范围: 205-215
• 页数: 11页
• 语种: 英文

摘要

In this study, we developed a system based on deep space–time neural networks for gesture recog-nition. When users change or the number of gesture categories increases, the accuracy of gesture recog-nition 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 ex-tract 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 dimen-sion. In the experiment, 50 types of gestures of five users were collected and evaluated. The experimen-tal results demonstrate the effectiveness of this sys-tem 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.