title: A Novel Phonology- and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography Sensors.
abstract: Sign language recognition (SLR) is an important communication tool between the deaf and the external world. It is highly necessary to develop a worldwide continuous and large-vocabulary-scale SLR system for practical usage. In this paper, we propose a novel phonology- and radical-coded Chinese SLR framework to demonstrate the feasibility of continuous SLR using accelerometer (ACC) and surface electromyography (sEMG) sensors. The continuous Chinese characters, consisting of coded sign gestures, are first segmented into active segments using EMG signals by means of moving average algorithm. Then, features of each component are extracted from both ACC and sEMG signals of active segments (i.e., palm orientation represented by the mean and variance of ACC signals, hand movement represented by the fixed-point ACC sequence, and hand shape represented by both the mean absolute value (MAV) and autoregressive model coefficients (ARs)). Afterwards, palm orientation is first classified, distinguishing "Palm Downward" sign gestures from "Palm Inward" ones. Only the "Palm Inward" gestures are sent for further hand movement and hand shape recognition by dynamic time warping (DTW) algorithm and hidden Markov models (HMM) respectively. Finally, component recognition results are integrated to identify one certain coded gesture. Experimental results demonstrate that the proposed SLR framework with a vocabulary scale of 223 characters can achieve an averaged recognition accuracy of 96.01% ± 0.83% for coded gesture recognition tasks and 92.73% ± 1.47% for character recognition tasks. Besides, it demonstrats that sEMG signals are rather consistent for a given hand shape independent of hand movements. Hence, the number of training samples will not be significantly increased when the vocabulary scale increases, since not only the number of the completely new proposed coded gestures is constant and limited, but also the transition movement which connects successive signs needs no training samples to model even though the same coded gesture performed in different characters. This work opens up a possible new way to realize a practical Chinese SLR system.
authors.affiliation: Department of Biomedical Engineering, Hefei University of Technology, 193 Tunxi Road, Hefei 230009, China. chengjuan@hfut.edu.cn.
authors.forename: Juan
authors.initials: J
authors.lastname: Cheng
authors.affiliation: Department of Biomedical Engineering, Hefei University of Technology, 193 Tunxi Road, Hefei 230009, China. xun.chen@hfut.edu.cn.|Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T-1Z4, Canada. xun.chen@hfut.edu.cn.
authors.forename: Xun
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T-1Z4, Canada. aipingl@ece.ubc.ca.
authors.forename: Aiping
authors.initials: A
authors.lastname: Liu
authors.affiliation: Department of Biomedical Engineering, Hefei University of Technology, 193 Tunxi Road, Hefei 230009, China. hpeng@hfut.edu.cn.
authors.forename: Hu
authors.initials: H
authors.lastname: Peng
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D000465:Algorithms; D015374:Biosensing Techniques / Q000295:instrumentation; D002681:China; D004576:Electromyography / Q000295:instrumentation*; D005240:Feasibility Studies; D005868:Gestures; D006225:Hand; D006801:Humans; D007802:Language; D010363:Pattern Recognition, Automated / Q000379:methods*; D010700:Phonetics; D012813:Sign Language; D012815:Signal Processing, Computer-Assisted
keywords: accelerometer; component level classification; dynamic time warping; electromyography; hidden markov model (HMM); sign language recognition