Interpreting sign components from accelerometer and sEMG data for automatic sign language recognition

Yun Li; Xiang Chen; Xu Zhang; Kongqiao Wang; Jihai Yang

doi:10.1109/IEMBS.2011.6090910

Interpreting sign components from accelerometer and sEMG data for automatic sign language recognition

Annu Int Conf IEEE Eng Med Biol Soc. 2011:2011:3358-61. doi: 10.1109/IEMBS.2011.6090910.

Authors

Yun Li¹, Xiang Chen, Xu Zhang, Kongqiao Wang, Jihai Yang

Affiliation

¹ Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. liyun5@mail.ustc.edu.cn

PMID: 22255059
DOI: 10.1109/IEMBS.2011.6090910

Abstract

The identification of constituent components of each sign gesture is a practical way of establishing large-vocabulary sign language recognition (SLR) system. Aiming at developing such a system using portable accelerometer (ACC) and surface electromyographic (sEMG) sensors, this work proposes a method for automatic SLR at the component level. The preliminary experimental results demonstrate the effectiveness of the proposed method and the feasibility of interpreting sign components from ACC and sEMG data. Our study improves the performance of SLR based on ACC and sEMG sensors and will promote the realization of a large-vocabulary portable SLR system.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Electromyography / methods*
Humans
Markov Chains
Sign Language*