Chinese sign language recognition based on surface electromyography and motion information

被引：0
|
作者：
Li, Wenyu [1 ]
Luo, Zhizeng [1 ]
Li, Wenguo [1 ,2 ]
Xi, Xugang [1 ]
机构：
[1] Hangzhou Dianzi Univ, Inst Intelligent Control & Robot, Hangzhou, Zhejiang, Peoples R China
[2] Xianheng Int Hangzhou Elect Mfg Co Ltd, Hangzhou, Zhejiang, Peoples R China
来源：
PLOS ONE | 2023年 / 18卷 / 12期
基金：
中国国家自然科学基金;
关键词：
MODEL;
D O I：
10.1371/journal.pone.0295398
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Sign language (SL) has strong structural features. Various gestures and the complex trajectories of hand movements bring challenges to sign language recognition (SLR). Based on the inherent correlation between gesture and trajectory of SL action, SLR is organically divided into gesture-based recognition and gesture-related movement trajectory recognition. One hundred and twenty commonly used Chinese SL words involving 9 gestures and 8 movement trajectories, are selected as research and test objects. The method based on the amplitude state of surface electromyography (sEMG) signal and acceleration signal is used for vocabulary segmentation. The multi-sensor decision fusion method of coupled hidden Markov model is used to complete the recognition of SL vocabulary, and the average recognition rate is 90.41%. Experiments show that the method of sEMG signal and motion information fusion has good practicability in SLR.
引用
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页数：15
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