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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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Autores principales: Sikandar, Tasriva, Rahman, Sam Matiur, Islam, Dilshad, Ali, Md. Asraf, Mamun, Md. Abdullah Al, Rabbi, Mohammad Fazle, Ghazali, Kamarul H., Altwijri, Omar, Almijalli, Mohammed, Ahamed, Nizam U.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687360/
https://www.ncbi.nlm.nih.gov/pubmed/36421116
http://dx.doi.org/10.3390/bioengineering9110715
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author Sikandar, Tasriva
Rahman, Sam Matiur
Islam, Dilshad
Ali, Md. Asraf
Mamun, Md. Abdullah Al
Rabbi, Mohammad Fazle
Ghazali, Kamarul H.
Altwijri, Omar
Almijalli, Mohammed
Ahamed, Nizam U.
author_facet Sikandar, Tasriva
Rahman, Sam Matiur
Islam, Dilshad
Ali, Md. Asraf
Mamun, Md. Abdullah Al
Rabbi, Mohammad Fazle
Ghazali, Kamarul H.
Altwijri, Omar
Almijalli, Mohammed
Ahamed, Nizam U.
author_sort Sikandar, Tasriva
collection PubMed
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.
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spelling pubmed-96873602022-11-25 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 Ghazali, Kamarul H. Altwijri, Omar Almijalli, Mohammed Ahamed, Nizam U. Bioengineering (Basel) Article 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 2022-11-19 /pmc/articles/PMC9687360/ /pubmed/36421116 http://dx.doi.org/10.3390/bioengineering9110715 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sikandar, Tasriva
Rahman, Sam Matiur
Islam, Dilshad
Ali, Md. Asraf
Mamun, Md. Abdullah Al
Rabbi, Mohammad Fazle
Ghazali, Kamarul H.
Altwijri, Omar
Almijalli, Mohammed
Ahamed, Nizam U.
Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687360/
https://www.ncbi.nlm.nih.gov/pubmed/36421116
http://dx.doi.org/10.3390/bioengineering9110715
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