Deep Learning Gesture Recognition System Based on 3D Convolution

The rapid development of the Internet of Things has promoted the progress of human-computer interaction technology, in which gesture recognition, as a key component, provides diversified applications for smart homes, elderly care, and other fields. In recent years, with the wide application of Wi-Fi, gesture recognition based on channel state information (CSI) has attracted much attention due to its advantages of flexibility, convenience, and cost-effectiveness. In this paper, we propose a gesture recognition system that includes an image conversion module and a 3D convolutional network recognition module. In the image conversion module, we treat the CSI subcarrier information from multiple receivers as a one-dimensional time series and convert them into images separately. Then by aggregating all the image data on the new depth dimension, we finally input the processed data into the 3D Convolutional network recognition module for gesture recognition. This module focuses on capturing changes in CSI information over time and space. We evaluated our model on a Widar3.0 open data set, and the experimental results show that the proposed method has a good effect in realizing cross-domain gesture recognition.