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Motion Capture Technologies for Ergonomics: A Systematic Literature Review

Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for man...

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Autores principales: Salisu, Sani, Ruhaiyem, Nur Intan Raihana, Eisa, Taiseer Abdalla Elfadil, Nasser, Maged, Saeed, Faisal, Younis, Hussain A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416907/
https://www.ncbi.nlm.nih.gov/pubmed/37568956
http://dx.doi.org/10.3390/diagnostics13152593
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author Salisu, Sani
Ruhaiyem, Nur Intan Raihana
Eisa, Taiseer Abdalla Elfadil
Nasser, Maged
Saeed, Faisal
Younis, Hussain A.
author_facet Salisu, Sani
Ruhaiyem, Nur Intan Raihana
Eisa, Taiseer Abdalla Elfadil
Nasser, Maged
Saeed, Faisal
Younis, Hussain A.
author_sort Salisu, Sani
collection PubMed
description Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for many people. Despite this, no state-of-the-art literature review on MoCap systems for human clinical, rehabilitation and ergonomic analysis has been conducted. A medical diagnosis using AI applies machine learning algorithms and motion capture technologies to analyze patient data, enhancing diagnostic accuracy, enabling early disease detection and facilitating personalized treatment plans. It revolutionizes healthcare by harnessing the power of data-driven insights for improved patient outcomes and efficient clinical decision-making. The current review aimed to investigate: (i) the most used MoCap systems for clinical use, ergonomics and rehabilitation, (ii) their application and (iii) the target population. We used preferred reporting items for systematic reviews and meta-analysis guidelines for the review. Google Scholar, PubMed, Scopus and Web of Science were used to search for relevant published articles. The articles obtained were scrutinized by reading the abstracts and titles to determine their inclusion eligibility. Accordingly, articles with insufficient or irrelevant information were excluded from the screening. The search included studies published between 2013 and 2023 (including additional criteria). A total of 40 articles were eligible for review. The selected articles were further categorized in terms of the types of MoCap used, their application and the domain of the experiments. This review will serve as a guide for researchers and organizational management.
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spelling pubmed-104169072023-08-12 Motion Capture Technologies for Ergonomics: A Systematic Literature Review Salisu, Sani Ruhaiyem, Nur Intan Raihana Eisa, Taiseer Abdalla Elfadil Nasser, Maged Saeed, Faisal Younis, Hussain A. Diagnostics (Basel) Review Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for many people. Despite this, no state-of-the-art literature review on MoCap systems for human clinical, rehabilitation and ergonomic analysis has been conducted. A medical diagnosis using AI applies machine learning algorithms and motion capture technologies to analyze patient data, enhancing diagnostic accuracy, enabling early disease detection and facilitating personalized treatment plans. It revolutionizes healthcare by harnessing the power of data-driven insights for improved patient outcomes and efficient clinical decision-making. The current review aimed to investigate: (i) the most used MoCap systems for clinical use, ergonomics and rehabilitation, (ii) their application and (iii) the target population. We used preferred reporting items for systematic reviews and meta-analysis guidelines for the review. Google Scholar, PubMed, Scopus and Web of Science were used to search for relevant published articles. The articles obtained were scrutinized by reading the abstracts and titles to determine their inclusion eligibility. Accordingly, articles with insufficient or irrelevant information were excluded from the screening. The search included studies published between 2013 and 2023 (including additional criteria). A total of 40 articles were eligible for review. The selected articles were further categorized in terms of the types of MoCap used, their application and the domain of the experiments. This review will serve as a guide for researchers and organizational management. MDPI 2023-08-04 /pmc/articles/PMC10416907/ /pubmed/37568956 http://dx.doi.org/10.3390/diagnostics13152593 Text en © 2023 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 Review
Salisu, Sani
Ruhaiyem, Nur Intan Raihana
Eisa, Taiseer Abdalla Elfadil
Nasser, Maged
Saeed, Faisal
Younis, Hussain A.
Motion Capture Technologies for Ergonomics: A Systematic Literature Review
title Motion Capture Technologies for Ergonomics: A Systematic Literature Review
title_full Motion Capture Technologies for Ergonomics: A Systematic Literature Review
title_fullStr Motion Capture Technologies for Ergonomics: A Systematic Literature Review
title_full_unstemmed Motion Capture Technologies for Ergonomics: A Systematic Literature Review
title_short Motion Capture Technologies for Ergonomics: A Systematic Literature Review
title_sort motion capture technologies for ergonomics: a systematic literature review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416907/
https://www.ncbi.nlm.nih.gov/pubmed/37568956
http://dx.doi.org/10.3390/diagnostics13152593
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