基于LSTM的CSI手势识别方法

CSI Gesture Recognition Method Based on LSTM

基于WiFi的信道状态信息(Channel State Information,CSI)的手势识别在人机交互中具有广泛的应用前景。目前,大多数的CSI手势识别方法需人工提取特征,特征提取的过程繁琐,且只能识别特定方向的手势,限制了人的活动范围。针对上述问题,提出了利用长短时记忆神经网络(Long Short-Term Memory,LSTM)训练的方法,设计了一个基于LSTM的CSI手势识别系统。该系统将采集到的CSI数据首先进行异常点去除、最优子载波选择和离散小波变化去噪等预处理操作;然后通过LSTM网络训练分类,无需人工提取手势特征;最终实现推、拉、左挥、右挥4种手势在4个不同方向的识别,平均准确率达到了82.75%。文中分别讨论了发送到接收端的距离与数据集大小对手势识别准确率的影响,并对比WiG和WiFinger方法识别4个方向手势的识别准确率,结果表明文中所提方法具有更高的识别效果。

Gesture recognition based on WiFi channel state information(CSI)has broad application prospects in human-computer interaction.At present,most methods require manual extraction of features,and the feature extraction process is cumbersome.It can only recognize gestures in a specific direction,which limits the range of people’s activities.To solve the above problems,this paper proposed a method based on Long Short-Term Memory(LSTM)training to design a CSI gesture recognition system based on LSTM.The system preprocesses the collected CSI data through such as abnormal point removal,optimal subcarrier selection and discrete wavelet variation denoising.The LSTM network trains the classification without manual extraction of gesture features.Finally,the recognition of four gestures is achieved,which are pushing,pulling,left swing and right swing in four different directions,and an average recognition accuracy of 82.75%is reached.This paper discussed the influence of the distance between sender and receiver and the size of the data set on the accuracy of gesture recognition,and compared the gestures in four directions by WiG and WiFinger.The results show that the proposed method has higher recognition effect.