Motion Capture Technologies for Ergonomics: A Systematic Literature Review
Salisu, Sani and Ruhaiyem, Nur Intan Raihana and Eisa, Taiseer Abdalla Elfadil and Nasser, Maged and Saeed, Faisal and Younis, Hussain A. (2023) Motion Capture Technologies for Ergonomics: A Systematic Literature Review. Diagnostics, 13 (15). p. 2593. ISSN 2075-4418
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Abstract
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.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.3390/diagnostics13152593 | ||||||
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| Uncontrolled Keywords: | MBased systems, MLess systems, IMS systems, EMG, shoulder, hands | ||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science | ||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Gemma Tonks | ||||||
| Date Deposited: | 15 Feb 2024 15:24 | ||||||
| Last Modified: | 15 Feb 2024 15:24 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/15201 |
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