Research Article
Wi-Fi Gesture Recognition Technology Based on Time-Frequency Features (Workshop)
@INPROCEEDINGS{10.1007/978-3-030-41117-6_33, author={Zengshan Tian and Mengtian Ren and Qing Jiang and Xiaoya Zhang}, title={Wi-Fi Gesture Recognition Technology Based on Time-Frequency Features (Workshop)}, proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part II}, proceedings_a={CHINACOM PART 2}, year={2020}, month={2}, keywords={Dynamic gesture recognition Discrete Wavelet Transform Principal Component Analysis Time-frequency domain features}, doi={10.1007/978-3-030-41117-6_33} }- Zengshan Tian
Mengtian Ren
Qing Jiang
Xiaoya Zhang
Year: 2020
Wi-Fi Gesture Recognition Technology Based on Time-Frequency Features (Workshop)
CHINACOM PART 2
Springer
DOI: 10.1007/978-3-030-41117-6_33
Abstract
With the rapid development of artificial intelligence, gesture recognition has become the focus of many countries for research. Gesture recognition using Wi-Fi signals has become the mainstream of gesture recognition because it does not require additional equipment and lighting conditions. Firstly, how to extract useful gesture signals in a complex indoor environment. In this paper, after de-noising the signal by Discrete Wavelet Transform (DWT) technology, Principal Component Analysis (PCA) is used to eliminate the problem of signal redundancy between multiple CSI subcarriers, further to remove noise. Secondly, the frequency domain features of the gesture signal are constructed by performing Short-Time Fourier Transform (STFT) on the denoised CSI amplitude signal. Then, the time domain features are combined with the frequency domain features, and the features are trained and classified using the Support Vector Machine (SVM) classification method to complete the training and recognition of gesture. The experimental results show that this paper can effectively identify gestures in complex indoor environments.
