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.

目前，从无处不在的商用WiFi网卡中收集信道状态信息（CSI）已经变得非常方便，并且可以通过分析CSI的变化来识别影响CSI的人的位置或活动。因此，基于CSI的无线传感技术受到广泛关注。然而，现有的基于CSI的手势识别方法仍存在子载波选择不优化、运动区间提取不够准确等问题，手势识别方法的准确率仍需进一步提高。针对上述问题，论文提出了一种基于错位平均绝对偏差（MMAD）和KL散度的手势识别方法，称为MMAD-KL-GR方法。该方法利用所提出的MMAD算法提取包含手势信息的CSI幅度区间，然后通过比较CSI幅度的KL散度来选择子载波，最后使用子空间K-最近邻（KNN）算法进行手势识别。多项实验表明，MMAD-KL-GR方法可以有效提高手势识别的准确率。