title: Interpreting sign components from accelerometer and sEMG data for automatic sign language recognition.
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.
authors.affiliation: Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. liyun5@mail.ustc.edu.cn
authors.forename: Yun
authors.initials: Y
authors.lastname: Li
authors.forename: Xiang
authors.initials: X
authors.lastname: Chen
authors.forename: Xu
authors.initials: X
authors.lastname: Zhang
authors.forename: Kongqiao
authors.initials: K
authors.lastname: Wang
authors.forename: Jihai
authors.initials: J
authors.lastname: Yang
mesh_terms: D004576:Electromyography / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D012813:Sign Language