Research Article
WiCLR: A Sign Language Recognition System Framework Based on Wireless Sensing
@INPROCEEDINGS{10.1007/978-3-030-36442-7_7, author={Wang Lin and Liu Yu and Jing Nan}, title={WiCLR: A Sign Language Recognition System Framework Based on Wireless Sensing}, proceedings={Broadband Communications, Networks, and Systems. 10th EAI International Conference, Broadnets 2019, Xi’an, China, October 27-28, 2019, Proceedings}, proceedings_a={BROADNETS}, year={2019}, month={12}, keywords={CSI Isolated sign language Activity recognition Wireless sensing}, doi={10.1007/978-3-030-36442-7_7} }- Wang Lin
Liu Yu
Jing Nan
Year: 2019
WiCLR: A Sign Language Recognition System Framework Based on Wireless Sensing
BROADNETS
Springer
DOI: 10.1007/978-3-030-36442-7_7
Abstract
The non-intrusion and device-free sign language recognition (SLR) is of great significance to improve the quality of life, broaden living space and enhance social service for the deaf and mute. In this paper, we propose a SLR system framework, called WiCLR, for identifying isolated words in Chinese sign language exploring the channel state information (CSI). WiCLR is made up entirely of commercial wireless devices, which does not incur significant deployment and maintenance overhead. In the framework we devise a signal denoising method to remove the environment noise and the internal state transitions in commercial devices. Moreover, we propose the multi-stream anomaly detection algorithm in action segmentation and fusion. Finally, the extreme learning machine (ELM) is utilized to meet the accuracy and real-time requirements. The experiment results show that the recognition accuracy of the approach reaches 94.3% and 91.7% respectively in an empty conference room and a laboratory.
