WiRN: Real-Time and Lightweight Gesture Detection System on Edge Device

Gesture detection based on WiFi signals does not require users to carry additional equipment, and can better protect the privacy of users during the detection process, so it has received widespread attention. However, the existing work does not consider the actual deployment of the platform, and ignores the requirements for the computing power of the platform and the actual reasoning delay, resulting in many methods that are not suitable for the use of edge devices. In this paper, we propose a WiFi gesture detection system, named WiRN, which is fully deployed on edge devices and does not require the participation of additional computing devices. In WiRN, We have proposed solutions to related problems. First of all, in order to solve the problem of large differences in multiple phase differences obtained in different scenarios due to over-sensitive phases and to improve the robustness and universality of the system, we propose a multi-antenna-based phase difference selection algorithm to find the most suitable phase difference. Then, we fuse the amplitude and phase difference of different dimensions and obtain more fine-grained input data to solve the problem of the inability to deploy complex neural networks to fully extract features due to the limitation of edge device computing power, so that the input data contains richer feature information. In this way, for the first time, we will improve the accuracy of network classification from the data source. We evaluated the system through a series of experiments, and the results showed that under the premise of satisfying the real-time calculation of edge devices, we achieved the same accuracy as the existing complex network by using the simplest two-layer neural network. The recognition accuracy of about 93% is achieved in different environments.