Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This a...

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Autores principales:	Hussein Sarwat, Hassan Sarwat, Shady A. Maged, Tamer H. Emara, Ahmed M. Elbokl, Mohammed Ibrahim Awad
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	data glove home rehabilitation machine learning IoT Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/3cbf5b1590504af8b8494c0ec829f9b1
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id	oai:doaj.org-article:3cbf5b1590504af8b8494c0ec829f9b1
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spelling	oai:doaj.org-article:3cbf5b1590504af8b8494c0ec829f9b12021-11-11T18:59:44ZDesign of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning10.3390/s212169481424-8220https://doaj.org/article/3cbf5b1590504af8b8494c0ec829f9b12021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6948https://doaj.org/toc/1424-8220The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This assistance will enable medical professionals to either better provide for patients with severe injuries or treat more patients. It also translates into financial assistance as well in the long run. This paper demonstrated an automated assessment system for in-home rehabilitation utilizing a data glove, a mobile application, and machine learning algorithms. The system can be used by poststroke patients with a high level of recovery to assess their performance. Furthermore, this assessment can be sent to a medical professional for supervision. Additionally, a comparison between two machine learning classifiers was performed on their assessment of physical exercises. The proposed system has an accuracy of 85% (±5.1%) with careful feature and classifier selection.Hussein SarwatHassan SarwatShady A. MagedTamer H. EmaraAhmed M. ElboklMohammed Ibrahim AwadMDPI AGarticledata glovehome rehabilitationmachine learningIoTChemical technologyTP1-1185ENSensors, Vol 21, Iss 6948, p 6948 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data glove home rehabilitation machine learning IoT Chemical technology TP1-1185
spellingShingle	data glove home rehabilitation machine learning IoT Chemical technology TP1-1185 Hussein Sarwat Hassan Sarwat Shady A. Maged Tamer H. Emara Ahmed M. Elbokl Mohammed Ibrahim Awad Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning
description	The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This assistance will enable medical professionals to either better provide for patients with severe injuries or treat more patients. It also translates into financial assistance as well in the long run. This paper demonstrated an automated assessment system for in-home rehabilitation utilizing a data glove, a mobile application, and machine learning algorithms. The system can be used by poststroke patients with a high level of recovery to assess their performance. Furthermore, this assessment can be sent to a medical professional for supervision. Additionally, a comparison between two machine learning classifiers was performed on their assessment of physical exercises. The proposed system has an accuracy of 85% (±5.1%) with careful feature and classifier selection.
format	article
author	Hussein Sarwat Hassan Sarwat Shady A. Maged Tamer H. Emara Ahmed M. Elbokl Mohammed Ibrahim Awad
author_facet	Hussein Sarwat Hassan Sarwat Shady A. Maged Tamer H. Emara Ahmed M. Elbokl Mohammed Ibrahim Awad
author_sort	Hussein Sarwat
title	Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning
title_short	Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning
title_full	Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning
title_fullStr	Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning
title_full_unstemmed	Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning
title_sort	design of a data glove for assessment of hand performance using supervised machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3cbf5b1590504af8b8494c0ec829f9b1
work_keys_str_mv	AT husseinsarwat designofadatagloveforassessmentofhandperformanceusingsupervisedmachinelearning AT hassansarwat designofadatagloveforassessmentofhandperformanceusingsupervisedmachinelearning AT shadyamaged designofadatagloveforassessmentofhandperformanceusingsupervisedmachinelearning AT tamerhemara designofadatagloveforassessmentofhandperformanceusingsupervisedmachinelearning AT ahmedmelbokl designofadatagloveforassessmentofhandperformanceusingsupervisedmachinelearning AT mohammedibrahimawad designofadatagloveforassessmentofhandperformanceusingsupervisedmachinelearning
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Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

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