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LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment

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

Computers in Biology and Medicine, Volume: 173, Start page: 108382

Swansea University Author: Sara Sharifzadeh Orcid Logo

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DOI (Published version): 10.1016/j.compbiomed.2024.108382

Abstract

Research evidence shows that physical rehabilitation exercises prescribed by medical experts can assist in restoring physical function, improving life quality, and promoting independence for physically disabled individuals. In response to the absence of immediate expert feedback on performed actions...

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Published in: Computers in Biology and Medicine
ISSN: 0010-4825
Published: Elsevier BV 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa66848
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In response to the absence of immediate expert feedback on performed actions, developing a Human Action Evaluation (HAE) system emerges as a valuable automated solution, addressing the need for accurate assessment of exercises and guidance during physical rehabilitation. Previous HAE systems developed for the rehabilitation exercises have focused on developing models that utilize skeleton data as input to compute a quality score for each action performed by the patient. However, existing studies have focused on improving scoring performance while often overlooking computational efficiency. In this research, we propose LightPRA (Light Physical Rehabilitation Assessment) system, an innovative architectural solution based on a Temporal Convolutional Network (TCN), which harnesses the capabilities of dilated causal Convolutional Neural Networks (CNNs). 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Through empirical analysis performed on the University of Idaho-Physical Rehabilitation Movement Data (UI-PRMD) and KInematic assessment of MOvement for remote monitoring of physical REhabilitation (KIMORE) datasets, our proposed LightPRA model demonstrates superior performance over several state-of-the-art approaches such as Spatial&#x2013;Temporal Graph Convolutional Network (STGCN) and Long Short-Term Memory (LSTM)-based models in scoring human activity performance, while exhibiting lower computational cost and complexity.</abstract><type>Journal Article</type><journal>Computers in Biology and Medicine</journal><volume>173</volume><journalNumber/><paginationStart>108382</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0010-4825</issnPrint><issnElectronic/><keywords>Activity evaluation; Dilated convolutions; Temporal Convolutional Network; Telerehabilitation; Skeleton data</keywords><publishedDay>1</publishedDay><publishedMonth>5</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-05-01</publishedDate><doi>10.1016/j.compbiomed.2024.108382</doi><url/><notes/><college>COLLEGE NANME</college><department>Mathematics and Computer Science School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MACS</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>The authors would like to thank Coventry University and Deakin University for jointly funding this Ph.D. project titled &#x201C;Activity Recognition Using Digital Frame Streams for Monitoring Rehab Period&#x201D;.</funders><projectreference/><lastEdited>2024-07-03T16:05:20.2390929</lastEdited><Created>2024-06-22T11:55:32.5888596</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>Seng W.</firstname><surname>Loke</surname><orcid>0000-0002-5339-9305</orcid><order>4</order></author><author><firstname>Vasile</firstname><surname>Palade</surname><order>5</order></author><author><firstname>Michael J.</firstname><surname>Duncan</surname><orcid>0000-0002-2016-6580</orcid><order>6</order></author><author><firstname>Bahareh</firstname><surname>Nakisa</surname><order>7</order></author></authors><documents><document><filename>66848__30806__01064112eb4f416694978b395f10b6fb.pdf</filename><originalFilename>66848.VoR.pdf</originalFilename><uploaded>2024-07-03T16:03:51.3108131</uploaded><type>Output</type><contentLength>2353580</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2024 The Authors. 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spelling 2024-07-03T16:05:20.2390929 v2 66848 2024-06-22 LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment a4e15f304398ecee3f28c7faec69c1b0 0000-0003-4621-2917 Sara Sharifzadeh Sara Sharifzadeh true false 2024-06-22 MACS Research evidence shows that physical rehabilitation exercises prescribed by medical experts can assist in restoring physical function, improving life quality, and promoting independence for physically disabled individuals. In response to the absence of immediate expert feedback on performed actions, developing a Human Action Evaluation (HAE) system emerges as a valuable automated solution, addressing the need for accurate assessment of exercises and guidance during physical rehabilitation. Previous HAE systems developed for the rehabilitation exercises have focused on developing models that utilize skeleton data as input to compute a quality score for each action performed by the patient. However, existing studies have focused on improving scoring performance while often overlooking computational efficiency. In this research, we propose LightPRA (Light Physical Rehabilitation Assessment) system, an innovative architectural solution based on a Temporal Convolutional Network (TCN), which harnesses the capabilities of dilated causal Convolutional Neural Networks (CNNs). This approach efficiently captures complex temporal features and characteristics of the skeleton data with lower computational complexity, making it suitable for real-time feedback provided on resource-constrained devices such as Internet of Things (IoT) devices and Edge computing frameworks. Through empirical analysis performed on the University of Idaho-Physical Rehabilitation Movement Data (UI-PRMD) and KInematic assessment of MOvement for remote monitoring of physical REhabilitation (KIMORE) datasets, our proposed LightPRA model demonstrates superior performance over several state-of-the-art approaches such as Spatial–Temporal Graph Convolutional Network (STGCN) and Long Short-Term Memory (LSTM)-based models in scoring human activity performance, while exhibiting lower computational cost and complexity. Journal Article Computers in Biology and Medicine 173 108382 Elsevier BV 0010-4825 Activity evaluation; Dilated convolutions; Temporal Convolutional Network; Telerehabilitation; Skeleton data 1 5 2024 2024-05-01 10.1016/j.compbiomed.2024.108382 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee The authors would like to thank Coventry University and Deakin University for jointly funding this Ph.D. project titled “Activity Recognition Using Digital Frame Streams for Monitoring Rehab Period”. 2024-07-03T16:05:20.2390929 2024-06-22T11:55:32.5888596 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 Seng W. Loke 0000-0002-5339-9305 4 Vasile Palade 5 Michael J. Duncan 0000-0002-2016-6580 6 Bahareh Nakisa 7 66848__30806__01064112eb4f416694978b395f10b6fb.pdf 66848.VoR.pdf 2024-07-03T16:03:51.3108131 Output 2353580 application/pdf Version of Record true © 2024 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment
spellingShingle LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment
Sara Sharifzadeh
title_short LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment
title_full LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment
title_fullStr LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment
title_full_unstemmed LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment
title_sort LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment
author_id_str_mv a4e15f304398ecee3f28c7faec69c1b0
author_id_fullname_str_mv a4e15f304398ecee3f28c7faec69c1b0_***_Sara Sharifzadeh
author Sara Sharifzadeh
author2 Sara Sardari
Sara Sharifzadeh
Alireza Daneshkhah
Seng W. Loke
Vasile Palade
Michael J. Duncan
Bahareh Nakisa
format Journal article
container_title Computers in Biology and Medicine
container_volume 173
container_start_page 108382
publishDate 2024
institution Swansea University
issn 0010-4825
doi_str_mv 10.1016/j.compbiomed.2024.108382
publisher Elsevier BV
college_str Faculty of Science and Engineering
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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
document_store_str 1
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description Research evidence shows that physical rehabilitation exercises prescribed by medical experts can assist in restoring physical function, improving life quality, and promoting independence for physically disabled individuals. In response to the absence of immediate expert feedback on performed actions, developing a Human Action Evaluation (HAE) system emerges as a valuable automated solution, addressing the need for accurate assessment of exercises and guidance during physical rehabilitation. Previous HAE systems developed for the rehabilitation exercises have focused on developing models that utilize skeleton data as input to compute a quality score for each action performed by the patient. However, existing studies have focused on improving scoring performance while often overlooking computational efficiency. In this research, we propose LightPRA (Light Physical Rehabilitation Assessment) system, an innovative architectural solution based on a Temporal Convolutional Network (TCN), which harnesses the capabilities of dilated causal Convolutional Neural Networks (CNNs). This approach efficiently captures complex temporal features and characteristics of the skeleton data with lower computational complexity, making it suitable for real-time feedback provided on resource-constrained devices such as Internet of Things (IoT) devices and Edge computing frameworks. Through empirical analysis performed on the University of Idaho-Physical Rehabilitation Movement Data (UI-PRMD) and KInematic assessment of MOvement for remote monitoring of physical REhabilitation (KIMORE) datasets, our proposed LightPRA model demonstrates superior performance over several state-of-the-art approaches such as Spatial–Temporal Graph Convolutional Network (STGCN) and Long Short-Term Memory (LSTM)-based models in scoring human activity performance, while exhibiting lower computational cost and complexity.
published_date 2024-05-01T08:09:55Z
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