Malaysian sign language recognition framework based on sensory glove

The purpose of this study was to propose a low-cost and real-time recognition system using asensory glove, which has 17 sensors with 65 channels to capture static sign data of the Malaysiansign language (MSL). The study uses an experimental design. Five participants well-known MSLwere chosen to perf...

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Main Author:	Altaha, Mohamed Aktham Ahmed
Format:	thesis
Language:	eng
Published:	2019
Subjects:	QA Mathematics
Online Access:	https://ir.upsi.edu.my/detailsg.php?det=5224
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id	oai:ir.upsi.edu.my:5224
record_format	uketd_dc
institution	Universiti Pendidikan Sultan Idris
collection	UPSI Digital Repository
language	eng
topic	QA Mathematics
spellingShingle	QA Mathematics Altaha, Mohamed Aktham Ahmed Malaysian sign language recognition framework based on sensory glove
description	The purpose of this study was to propose a low-cost and real-time recognition system using asensory glove, which has 17 sensors with 65 channels to capture static sign data of the Malaysiansign language (MSL). The study uses an experimental design. Five participants well-known MSLwere chosen to perform 75 gestures throughout wear sensory glove. This research was carriedout in six phases as follows: Phase I involved a review of literature via a systematic reviewapproach to identify the relevant set of articles that helped formulate the research questions.Phase II focused on the analysis of hand anatomy, hand kinematic, and hand gestures to helpunderstand the nature of MSL and to define the glove requirements. In Phase III, DataGlove wasdesigned and developed based on the glove requirements to help optimize the best functions of theglove. Phase IV involved the pre-processing, feature extraction, and classification of the datacollected from the proposed DataGlove and identified gestures of MSL. A new vision and sensor-basedMSL datasets were collected in Phase V. Phase VI focused on the evaluation and validation processacross different development stages. The error rate was used to check system performance. Also, a3D printed humanoid arm was used to validate the sensor mounted on the glove. The results of dataanalysis showed 37 common patterns with similar hand gestures in MSL. Furthermore, thedesign of DataGlove based on MSL analysis was effective in capturing a wide range of gestures witha recognition accuracy of 99%, 96%, and 93.4% for numbers, alphabet letters, and words,respectively. In conclusion, the research findings suggest that 37 group's gestures of MSLcan increase the recognition accuracy of MSL hand gestures to bridge the gap between peoplewith hearing impairments and ordinary people. For future research, a more comprehensive analysis of the MSL recognition system isrecommended.
format	thesis
qualification_name
qualification_level	Doctorate
author	Altaha, Mohamed Aktham Ahmed
author_facet	Altaha, Mohamed Aktham Ahmed
author_sort	Altaha, Mohamed Aktham Ahmed
title	Malaysian sign language recognition framework based on sensory glove
title_short	Malaysian sign language recognition framework based on sensory glove
title_full	Malaysian sign language recognition framework based on sensory glove
title_fullStr	Malaysian sign language recognition framework based on sensory glove
title_full_unstemmed	Malaysian sign language recognition framework based on sensory glove
title_sort	malaysian sign language recognition framework based on sensory glove
granting_institution	Universiti Pendidikan Sultan Idris
granting_department	Fakulti Seni, Komputeran dan Industri Kreatif
publishDate	2019
url	https://ir.upsi.edu.my/detailsg.php?det=5224
_version_	1747833173054586880
spelling	oai:ir.upsi.edu.my:52242020-09-09 Malaysian sign language recognition framework based on sensory glove 2019 Altaha, Mohamed Aktham Ahmed QA Mathematics The purpose of this study was to propose a low-cost and real-time recognition system using asensory glove, which has 17 sensors with 65 channels to capture static sign data of the Malaysiansign language (MSL). The study uses an experimental design. Five participants well-known MSLwere chosen to perform 75 gestures throughout wear sensory glove. This research was carriedout in six phases as follows: Phase I involved a review of literature via a systematic reviewapproach to identify the relevant set of articles that helped formulate the research questions.Phase II focused on the analysis of hand anatomy, hand kinematic, and hand gestures to helpunderstand the nature of MSL and to define the glove requirements. In Phase III, DataGlove wasdesigned and developed based on the glove requirements to help optimize the best functions of theglove. Phase IV involved the pre-processing, feature extraction, and classification of the datacollected from the proposed DataGlove and identified gestures of MSL. A new vision and sensor-basedMSL datasets were collected in Phase V. Phase VI focused on the evaluation and validation processacross different development stages. The error rate was used to check system performance. Also, a3D printed humanoid arm was used to validate the sensor mounted on the glove. The results of dataanalysis showed 37 common patterns with similar hand gestures in MSL. Furthermore, thedesign of DataGlove based on MSL analysis was effective in capturing a wide range of gestures witha recognition accuracy of 99%, 96%, and 93.4% for numbers, alphabet letters, and words,respectively. In conclusion, the research findings suggest that 37 group's gestures of MSLcan increase the recognition accuracy of MSL hand gestures to bridge the gap between peoplewith hearing impairments and ordinary people. For future research, a more comprehensive analysis of the MSL recognition system isrecommended. 2019 thesis https://ir.upsi.edu.my/detailsg.php?det=5224 https://ir.upsi.edu.my/detailsg.php?det=5224 text eng closedAccess Doctoral Universiti Pendidikan Sultan Idris Fakulti Seni, Komputeran dan Industri Kreatif Abdulla, D., Abdulla, S., Manaf, R., & Jarndal, A. H. (2016). Design andimplementation of a sign-to-speech/text system for deaf and dumb people. Paper presented at theElectronic Devices, Systems and Applications (ICEDSA), 2016 5th International Conference on.Abdulnabi, M., Al-Haiqi, A., Kiah, M. L. M., Zaidan, A., Zaidan, B., & Hussain, M. (2017). 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Malaysian sign language recognition framework based on sensory glove

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