No Cover Image

Journal article 21895 views 154 downloads

Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review

Sara Sardari Orcid Logo, Sara Sharifzadeh Orcid Logo, Alireza Daneshkhah, Bahareh Nakisa, Seng W. Loke Orcid Logo, Vasile Palade Orcid Logo, Michael J. Duncan Orcid Logo

Computers in Biology and Medicine, Volume: 158, Start page: 106835

Swansea University Author: Sara Sharifzadeh Orcid Logo

  • 63069 VOR.pdf

    PDF | Version of Record

    This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

    Download (3.29MB)

Check full text

DOI (Published version): 10.1016/j.compbiomed.2023.106835

Abstract

Performing prescribed physical exercises during home-based rehabilitation programs plays an important role in regaining muscle strength and improving balance for people with different physical disabilities. However, patients attending these programs are not able to assess their action performance in...

Full description

Published in: Computers in Biology and Medicine
ISSN: 0010-4825
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa63069
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2023-04-03T14:29:00Z
last_indexed 2023-04-04T03:26:08Z
id cronfa63069
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>63069</id><entry>2023-04-03</entry><title>Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review</title><swanseaauthors><author><sid>a4e15f304398ecee3f28c7faec69c1b0</sid><ORCID>0000-0003-4621-2917</ORCID><firstname>Sara</firstname><surname>Sharifzadeh</surname><name>Sara Sharifzadeh</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2023-04-03</date><deptcode>SCS</deptcode><abstract>Performing prescribed physical exercises during home-based rehabilitation programs plays an important role in regaining muscle strength and improving balance for people with different physical disabilities. However, patients attending these programs are not able to assess their action performance in the absence of a medical expert. Recently, vision-based sensors have been deployed in the activity monitoring domain. They are capable of capturing accurate skeleton data. Furthermore, there have been significant advancements in Computer Vision (CV) and Deep Learning (DL) methodologies. These factors have promoted the solutions for designing automatic patient’s activity monitoring models. Then, improving such systems’ performance to assist patients and physiotherapists has attracted wide interest of the research community. This paper provides a comprehensive and up-to-date literature review on different stages of skeleton data acquisition processes for the aim of physio exercise monitoring. Then, the previously reported Artificial Intelligence (AI) - based methodologies for skeleton data analysis will be reviewed. In particular, feature learning from skeleton data, evaluation, and feedback generation for the purpose of rehabilitation monitoring will be studied. Furthermore, the associated challenges to these processes will be reviewed. Finally, the paper puts forward several suggestions for future research directions in this area.</abstract><type>Journal Article</type><journal>Computers in Biology and Medicine</journal><volume>158</volume><journalNumber/><paginationStart>106835</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0010-4825</issnPrint><issnElectronic/><keywords>Activity evaluation; Activity recognition; Computer vision; Deep learning; Physical rehabilitation; Skeleton data.</keywords><publishedDay>1</publishedDay><publishedMonth>5</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-05-01</publishedDate><doi>10.1016/j.compbiomed.2023.106835</doi><url>http://dx.doi.org/10.1016/j.compbiomed.2023.106835</url><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders/><projectreference/><lastEdited>2023-05-22T15:23:51.7277375</lastEdited><Created>2023-04-03T15:21:47.5738209</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Sara</firstname><surname>Sardari</surname><orcid>0000-0002-0042-5928</orcid><order>1</order></author><author><firstname>Sara</firstname><surname>Sharifzadeh</surname><orcid>0000-0003-4621-2917</orcid><order>2</order></author><author><firstname>Alireza</firstname><surname>Daneshkhah</surname><order>3</order></author><author><firstname>Bahareh</firstname><surname>Nakisa</surname><order>4</order></author><author><firstname>Seng W.</firstname><surname>Loke</surname><orcid>0000-0002-5339-9305</orcid><order>5</order></author><author><firstname>Vasile</firstname><surname>Palade</surname><orcid>0000-0002-6768-8394</orcid><order>6</order></author><author><firstname>Michael J.</firstname><surname>Duncan</surname><orcid>0000-0002-2016-6580</orcid><order>7</order></author></authors><documents><document><filename>63069__27241__80ade5d03f82466593f995c7f13cde8d.pdf</filename><originalFilename>63069 VOR.pdf</originalFilename><uploaded>2023-04-27T12:47:00.3544284</uploaded><type>Output</type><contentLength>3446390</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling v2 63069 2023-04-03 Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review a4e15f304398ecee3f28c7faec69c1b0 0000-0003-4621-2917 Sara Sharifzadeh Sara Sharifzadeh true false 2023-04-03 SCS Performing prescribed physical exercises during home-based rehabilitation programs plays an important role in regaining muscle strength and improving balance for people with different physical disabilities. However, patients attending these programs are not able to assess their action performance in the absence of a medical expert. Recently, vision-based sensors have been deployed in the activity monitoring domain. They are capable of capturing accurate skeleton data. Furthermore, there have been significant advancements in Computer Vision (CV) and Deep Learning (DL) methodologies. These factors have promoted the solutions for designing automatic patient’s activity monitoring models. Then, improving such systems’ performance to assist patients and physiotherapists has attracted wide interest of the research community. This paper provides a comprehensive and up-to-date literature review on different stages of skeleton data acquisition processes for the aim of physio exercise monitoring. Then, the previously reported Artificial Intelligence (AI) - based methodologies for skeleton data analysis will be reviewed. In particular, feature learning from skeleton data, evaluation, and feedback generation for the purpose of rehabilitation monitoring will be studied. Furthermore, the associated challenges to these processes will be reviewed. Finally, the paper puts forward several suggestions for future research directions in this area. Journal Article Computers in Biology and Medicine 158 106835 Elsevier BV 0010-4825 Activity evaluation; Activity recognition; Computer vision; Deep learning; Physical rehabilitation; Skeleton data. 1 5 2023 2023-05-01 10.1016/j.compbiomed.2023.106835 http://dx.doi.org/10.1016/j.compbiomed.2023.106835 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University SU Library paid the OA fee (TA Institutional Deal) 2023-05-22T15:23:51.7277375 2023-04-03T15:21:47.5738209 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Sara Sardari 0000-0002-0042-5928 1 Sara Sharifzadeh 0000-0003-4621-2917 2 Alireza Daneshkhah 3 Bahareh Nakisa 4 Seng W. Loke 0000-0002-5339-9305 5 Vasile Palade 0000-0002-6768-8394 6 Michael J. Duncan 0000-0002-2016-6580 7 63069__27241__80ade5d03f82466593f995c7f13cde8d.pdf 63069 VOR.pdf 2023-04-27T12:47:00.3544284 Output 3446390 application/pdf Version of Record true This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) true eng http://creativecommons.org/licenses/by/4.0/
title Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review
spellingShingle Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review
Sara Sharifzadeh
title_short Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review
title_full Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review
title_fullStr Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review
title_full_unstemmed Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review
title_sort Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review
author_id_str_mv a4e15f304398ecee3f28c7faec69c1b0
author_id_fullname_str_mv a4e15f304398ecee3f28c7faec69c1b0_***_Sara Sharifzadeh
author Sara Sharifzadeh
author2 Sara Sardari
Sara Sharifzadeh
Alireza Daneshkhah
Bahareh Nakisa
Seng W. Loke
Vasile Palade
Michael J. Duncan
format Journal article
container_title Computers in Biology and Medicine
container_volume 158
container_start_page 106835
publishDate 2023
institution Swansea University
issn 0010-4825
doi_str_mv 10.1016/j.compbiomed.2023.106835
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.1016/j.compbiomed.2023.106835
document_store_str 1
active_str 0
description Performing prescribed physical exercises during home-based rehabilitation programs plays an important role in regaining muscle strength and improving balance for people with different physical disabilities. However, patients attending these programs are not able to assess their action performance in the absence of a medical expert. Recently, vision-based sensors have been deployed in the activity monitoring domain. They are capable of capturing accurate skeleton data. Furthermore, there have been significant advancements in Computer Vision (CV) and Deep Learning (DL) methodologies. These factors have promoted the solutions for designing automatic patient’s activity monitoring models. Then, improving such systems’ performance to assist patients and physiotherapists has attracted wide interest of the research community. This paper provides a comprehensive and up-to-date literature review on different stages of skeleton data acquisition processes for the aim of physio exercise monitoring. Then, the previously reported Artificial Intelligence (AI) - based methodologies for skeleton data analysis will be reviewed. In particular, feature learning from skeleton data, evaluation, and feedback generation for the purpose of rehabilitation monitoring will be studied. Furthermore, the associated challenges to these processes will be reviewed. Finally, the paper puts forward several suggestions for future research directions in this area.
published_date 2023-05-01T15:23:49Z
_version_ 1766604595533971456
score 11.027473

Similar Items