Dynamic Hand Gesture Detection and Recognition with WiFi Signal Based on 1D-CNN

Due to the rapid development of Internet of Things (IoT) technology and artificial intelligence, there is an urgent need for human-computer interaction (HCI) applications. The dynamic hand gesture recognition technology based on WiFi signal plays an important role. However, although the gesture recognition system using Channel State Information (CSI) has made great progress, we have observed that in the current research, most commercial network cards can not directly extract such signals, and the easily acquired received signal strength (RSS) can only recognize simple gestures. Therefore, in this paper, we present a universal framework to achieve dynamic hand gesture detection and recognition with RSS. We use RSS from multiple independent WiFi nodes to increase the upper limit of recognize capability, enabling the system to recognize seven complex dynamic hand gestures. The false trigger detection algorithm effectively eliminates the false triggers, and the detection accuracy is close to 91.38%. The system uses a state machine and a linear scale algorithm to accommodate different hand gesture speed with durations ranging from 0.9s to 5.4s. Furthermore, we analyze the errors of the detection algorithm and propose a recognition architecture based on One Dimension Convolutional Neural Network (1D-CNN) and two data collection strategies: gesture extending and gesture shifting. The proposed 1D-CNN effectively overcomes the error caused by hand gesture detection algorithm and the recognition accuracy reaches 86.91%. Combined with the gesture shifting strategy, the recognition accuracy is further improved to 93.03%.