Air-CSL: Chinese Sign Language Recognition Based on the Commercial WiFi Devices

Abstract

Artificial intelligence and Internet of Things (IoT) devices are experiencing explosive growth. Currently, the commonly used gesture recognition methods are difficult to deploy and expensive, so this paper uses the Channel State Information (CSI) for Chinese sign language recognition. Aiming at the problems of current gesture recognition methods, such as strong personnel dependence, high computational resource consumption, and low robustness, we proposed a Chinese sign language gesture recognition method named Air-CSL. In this method, the Local Outlier Factor (LOF) removal algorithm and the Discrete Wavelet Transform (DWT) are used to reduce the noise in the data, and the subcarriers that best represent the gesture data are selected by principal component analysis. After denoising, mathematical statistics were extracted from the gesture waveform as the eigenvalues, and the features were fused by the Deep Restricted Boltzmann Machine (DBM). Finally, the result of gesture classification and recognition is obtained by the Gated Recurrent Unit (GRU). In this way, the prediction model realizes as well as the classification of sign language gestures. The results show that the proposed method can effectively recognize Chinese sign language gestures of different people in different environments and has good robustness.