基于时频特征的Wi-Fi手势识别技术

随着人工智能的快速发展,手势识别已经成为重点关注和研究的对象,利用Wi-Fi信号进行手势识别的技术无需额外的设备以及光照的条件,逐渐成为手势识别的主流.针对传统基于时域统计特征的方法,将经过噪声抑制后的信号进行短时傅里叶变换(short-time Fourier transform,STFT)构建手势信号的频域特征,结合时域和频域特性选择动作持续时间、频谱熵等特征作为特征值,采用支持向量机(support vector machine,SVM)的分类方法对特征值进行训练并完成手势识别.实验结果表明,在复杂的室内环境与室外空旷环境均能有效对手势进行识别,手势的识别率达90％以上.

Wi-Fi gesture recognition technology based on time-frequency features

With the rapid development of artificial intelligence, gesture recognition has become the focus of attention and research. Gesture recognition using Wi-Fi signals has become the mainstream of gesture recognition because it does not require additional equipment and lighting conditions. For gesture recognition method based on traditional time domain statistical features, in this paper, the short-time Fourier transform (STFT) of the denoised signal is used to construct the frequency domain features. Then, combining time domain and frequency domain features, selecting features such as action duration and spectral entropy as features, and the features are performed by using the support vector machine (SVM) classification method to train and complete gesture recognition. The experimental results show that this paper can effectively identify the gestures in the complex indoor environment and outdoor environment. The recognition rate of gestures is over 90%.