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Walking speed classiﬁcation from marker-free video images in two-dimension using optimum data and a deep learning method

Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intell...

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Main Authors:	Sikandar, Tasriva, Rahman, Sam Matiur, Islam, Dilshad, Ali, Md Asraf, Mamun, Md Abdullah Al, Rabbi, Mohammad Fazle, Kamarul Hawari, Ghazali, Altwijri, Omar, Almijalli, Mohammed, Ahamed, Nizam Uddin
Format:	Article
Language:	English
Published:	MDPI 2022
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/40599/1/Walking%20speed%20classification%20from%20marker-free%20video.pdf http://umpir.ump.edu.my/id/eprint/40599/ https://doi.org/10.3390/bioengineering9110715 https://doi.org/10.3390/bioengineering9110715
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spelling	my.ump.umpir.405992024-04-30T06:32:04Z http://umpir.ump.edu.my/id/eprint/40599/ Walking speed classiﬁcation from marker-free video images in two-dimension using optimum data and a deep learning method Sikandar, Tasriva Rahman, Sam Matiur Islam, Dilshad Ali, Md Asraf Mamun, Md Abdullah Al Rabbi, Mohammad Fazle Kamarul Hawari, Ghazali Altwijri, Omar Almijalli, Mohammed Ahamed, Nizam Uddin T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method. MDPI 2022-11 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/40599/1/Walking%20speed%20classification%20from%20marker-free%20video.pdf Sikandar, Tasriva and Rahman, Sam Matiur and Islam, Dilshad and Ali, Md Asraf and Mamun, Md Abdullah Al and Rabbi, Mohammad Fazle and Kamarul Hawari, Ghazali and Altwijri, Omar and Almijalli, Mohammed and Ahamed, Nizam Uddin (2022) Walking speed classiﬁcation from marker-free video images in two-dimension using optimum data and a deep learning method. Bioengineering, 9 (715). pp. 1-13. ISSN 2306-5354. (Published) https://doi.org/10.3390/bioengineering9110715 https://doi.org/10.3390/bioengineering9110715
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Sikandar, Tasriva Rahman, Sam Matiur Islam, Dilshad Ali, Md Asraf Mamun, Md Abdullah Al Rabbi, Mohammad Fazle Kamarul Hawari, Ghazali Altwijri, Omar Almijalli, Mohammed Ahamed, Nizam Uddin Walking speed classiﬁcation from marker-free video images in two-dimension using optimum data and a deep learning method
description	Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method.
format	Article
author	Sikandar, Tasriva Rahman, Sam Matiur Islam, Dilshad Ali, Md Asraf Mamun, Md Abdullah Al Rabbi, Mohammad Fazle Kamarul Hawari, Ghazali Altwijri, Omar Almijalli, Mohammed Ahamed, Nizam Uddin
author_facet	Sikandar, Tasriva Rahman, Sam Matiur Islam, Dilshad Ali, Md Asraf Mamun, Md Abdullah Al Rabbi, Mohammad Fazle Kamarul Hawari, Ghazali Altwijri, Omar Almijalli, Mohammed Ahamed, Nizam Uddin
author_sort	Sikandar, Tasriva
title	Walking speed classiﬁcation from marker-free video images in two-dimension using optimum data and a deep learning method
title_short	Walking speed classiﬁcation from marker-free video images in two-dimension using optimum data and a deep learning method
title_full	Walking speed classiﬁcation from marker-free video images in two-dimension using optimum data and a deep learning method
title_fullStr	Walking speed classiﬁcation from marker-free video images in two-dimension using optimum data and a deep learning method
title_full_unstemmed	Walking speed classiﬁcation from marker-free video images in two-dimension using optimum data and a deep learning method
title_sort	walking speed classiﬁcation from marker-free video images in two-dimension using optimum data and a deep learning method
publisher	MDPI
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/40599/1/Walking%20speed%20classification%20from%20marker-free%20video.pdf http://umpir.ump.edu.my/id/eprint/40599/ https://doi.org/10.3390/bioengineering9110715 https://doi.org/10.3390/bioengineering9110715
_version_	1822924244120502272
score	13.232414

Walking speed classiﬁcation from marker-free video images in two-dimension using optimum data and a deep learning method

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