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A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework

Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors, accelerometers (ACC), and gyroscopes (GYRO). I...

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Autores principales: Wei, Shengjing, Chen, Xiang, Yang, Xidong, Cao, Shuai, Zhang, Xu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851070/
https://www.ncbi.nlm.nih.gov/pubmed/27104534
http://dx.doi.org/10.3390/s16040556
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author Wei, Shengjing
Chen, Xiang
Yang, Xidong
Cao, Shuai
Zhang, Xu
author_facet Wei, Shengjing
Chen, Xiang
Yang, Xidong
Cao, Shuai
Zhang, Xu
author_sort Wei, Shengjing
collection PubMed
description Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors, accelerometers (ACC), and gyroscopes (GYRO). In this framework, a sign word was considered to be a combination of five common sign components, including hand shape, axis, orientation, rotation, and trajectory, and sign classification was implemented based on the recognition of five components. Especially, the proposed SLR framework consisted of two major parts. The first part was to obtain the component-based form of sign gestures and establish the code table of target sign gesture set using data from a reference subject. In the second part, which was designed for new users, component classifiers were trained using a training set suggested by the reference subject and the classification of unknown gestures was performed with a code matching method. Five subjects participated in this study and recognition experiments under different size of training sets were implemented on a target gesture set consisting of 110 frequently-used Chinese Sign Language (CSL) sign words. The experimental results demonstrated that the proposed framework can realize large-scale gesture set recognition with a small-scale training set. With the smallest training sets (containing about one-third gestures of the target gesture set) suggested by two reference subjects, (82.6 ± 13.2)% and (79.7 ± 13.4)% average recognition accuracy were obtained for 110 words respectively, and the average recognition accuracy climbed up to (88 ± 13.7)% and (86.3 ± 13.7)% when the training set included 50~60 gestures (about half of the target gesture set). The proposed framework can significantly reduce the user’s training burden in large-scale gesture recognition, which will facilitate the implementation of a practical SLR system.
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spelling pubmed-48510702016-05-04 A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework Wei, Shengjing Chen, Xiang Yang, Xidong Cao, Shuai Zhang, Xu Sensors (Basel) Article Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors, accelerometers (ACC), and gyroscopes (GYRO). In this framework, a sign word was considered to be a combination of five common sign components, including hand shape, axis, orientation, rotation, and trajectory, and sign classification was implemented based on the recognition of five components. Especially, the proposed SLR framework consisted of two major parts. The first part was to obtain the component-based form of sign gestures and establish the code table of target sign gesture set using data from a reference subject. In the second part, which was designed for new users, component classifiers were trained using a training set suggested by the reference subject and the classification of unknown gestures was performed with a code matching method. Five subjects participated in this study and recognition experiments under different size of training sets were implemented on a target gesture set consisting of 110 frequently-used Chinese Sign Language (CSL) sign words. The experimental results demonstrated that the proposed framework can realize large-scale gesture set recognition with a small-scale training set. With the smallest training sets (containing about one-third gestures of the target gesture set) suggested by two reference subjects, (82.6 ± 13.2)% and (79.7 ± 13.4)% average recognition accuracy were obtained for 110 words respectively, and the average recognition accuracy climbed up to (88 ± 13.7)% and (86.3 ± 13.7)% when the training set included 50~60 gestures (about half of the target gesture set). The proposed framework can significantly reduce the user’s training burden in large-scale gesture recognition, which will facilitate the implementation of a practical SLR system. MDPI 2016-04-19 /pmc/articles/PMC4851070/ /pubmed/27104534 http://dx.doi.org/10.3390/s16040556 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wei, Shengjing
Chen, Xiang
Yang, Xidong
Cao, Shuai
Zhang, Xu
A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework
title A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework
title_full A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework
title_fullStr A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework
title_full_unstemmed A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework
title_short A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework
title_sort component-based vocabulary-extensible sign language gesture recognition framework
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851070/
https://www.ncbi.nlm.nih.gov/pubmed/27104534
http://dx.doi.org/10.3390/s16040556
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