Gesture recognition method based on misalignment mean absolute deviation and KL divergence

Abstract

At present, it has become very convenient to collect channel state information (CSI) from ubiquitous commercial WiFi network cards, and the location or activity of a human who affects the CSI can be recognized by analyzing the change of the CSI. Therefore, wireless sensing technology based on the CSI has received widespread attention. However, the existing CSI-based gesture recognition methods still have some problems, which include that subcarrier selection is not optimized and motion interval extraction is not accurate enough, so the accuracy of gesture recognition methods still needs to be further improved. In response to the above problems, a gesture recognition method based on misalignment mean absolute deviation (MMAD) and KL divergence is proposed in the paper, which is called MMAD-KL-GR method. This method uses the proposed MMAD algorithm to extract the CSI amplitude intervals containing gesture information, then selects subcarriers by comparing the KL divergence of the CSI amplitude, and finally uses the subspace K-nearest neighbor (KNN) algorithm to recognize the gestures. Several experiments show that the MMAD-KL-GR method can effectively improve the accuracy of the gesture recognition.