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Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review
In biomechanics, joint kinetics has an important role in evaluating the mechanical load of the joint and understanding its motor function. Although an optical motion capture (OMC) system has mainly been used to evaluate joint kinetics in combination with force plates, inertial motion capture (IMC) s...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002742/ https://www.ncbi.nlm.nih.gov/pubmed/35408121 http://dx.doi.org/10.3390/s22072507 |
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author | Lee, Chang June Lee, Jung Keun |
author_facet | Lee, Chang June Lee, Jung Keun |
author_sort | Lee, Chang June |
collection | PubMed |
description | In biomechanics, joint kinetics has an important role in evaluating the mechanical load of the joint and understanding its motor function. Although an optical motion capture (OMC) system has mainly been used to evaluate joint kinetics in combination with force plates, inertial motion capture (IMC) systems have recently been emerging in joint kinetic analysis due to their wearability and ubiquitous measurement capability. In this regard, numerous studies have been conducted to estimate joint kinetics using IMC-based wearable systems. However, these have not been comprehensively addressed yet. Thus, the aim of this review is to explore the methodology of the current studies on estimating joint kinetic variables by means of an IMC system. From a systematic search of the literature, 48 studies were selected. This paper summarizes the content of the selected literature in terms of the (i) study characteristics, (ii) methodologies, and (iii) study results. The estimation methods of the selected studies are categorized into two types: the inverse dynamics-based method and the machine learning-based method. While these two methods presented different characteristics in estimating the kinetic variables, it was demonstrated in the literature that both methods could be applied with good performance for the kinetic analysis of joints in different daily activities. |
format | Online Article Text |
id | pubmed-9002742 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90027422022-04-13 Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review Lee, Chang June Lee, Jung Keun Sensors (Basel) Systematic Review In biomechanics, joint kinetics has an important role in evaluating the mechanical load of the joint and understanding its motor function. Although an optical motion capture (OMC) system has mainly been used to evaluate joint kinetics in combination with force plates, inertial motion capture (IMC) systems have recently been emerging in joint kinetic analysis due to their wearability and ubiquitous measurement capability. In this regard, numerous studies have been conducted to estimate joint kinetics using IMC-based wearable systems. However, these have not been comprehensively addressed yet. Thus, the aim of this review is to explore the methodology of the current studies on estimating joint kinetic variables by means of an IMC system. From a systematic search of the literature, 48 studies were selected. This paper summarizes the content of the selected literature in terms of the (i) study characteristics, (ii) methodologies, and (iii) study results. The estimation methods of the selected studies are categorized into two types: the inverse dynamics-based method and the machine learning-based method. While these two methods presented different characteristics in estimating the kinetic variables, it was demonstrated in the literature that both methods could be applied with good performance for the kinetic analysis of joints in different daily activities. MDPI 2022-03-25 /pmc/articles/PMC9002742/ /pubmed/35408121 http://dx.doi.org/10.3390/s22072507 Text en © 2022 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 | Systematic Review Lee, Chang June Lee, Jung Keun Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review |
title | Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review |
title_full | Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review |
title_fullStr | Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review |
title_full_unstemmed | Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review |
title_short | Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review |
title_sort | inertial motion capture-based wearable systems for estimation of joint kinetics: a systematic review |
topic | Systematic Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002742/ https://www.ncbi.nlm.nih.gov/pubmed/35408121 http://dx.doi.org/10.3390/s22072507 |
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