Abstract
Object detection algorithms based on convolutional neural networks are generally suitable for static gesture recognition. For actual hand gesture scenes, dynamic gestures are also widely used. A dynamic hand gesture recognition algorithm based on Channel State Information (CSI) and You Only Look Once: Version 3 (YOLOv3) is proposed for continuous dynamic hand gesture recognition. The data acquisition adopts a CSI-based radio frequency method. The adaptive weighted fusion, Kalman filtering, threshold segmentation and data conversion are used to generate gray value images. Finally, the YOLOv3 object detection algorithm is used to train and identify the grayscale image which include the information of continuous dynamic hand gestures. The effectiveness of the proposed method is verified by the recognition confusion matrix. And the proposed method has an average recognition accuracy of 94% for four custom dynamic hand gestures.
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