Inertial Motion Capture-Based Whole-Body Inverse Dynamics

Inertial Motion Capture (IMC) systems enable in situ studies of human motion free of the severe constraints imposed by Optical Motion Capture systems. Inverse dynamics can use those motions to estimate forces and moments developing within muscles and joints. We developed an inverse dynamic whole-bod...

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Autores principales:	Mohsen M. Diraneyya, JuHyeong Ryu, Eihab Abdel-Rahman, Carl T. Haas
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	inertial motion capture (IMC) inverse dynamics joint load ergonomics physical exposure Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/5ff678ec2ff948c19ad7a028189eefa3
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id	oai:doaj.org-article:5ff678ec2ff948c19ad7a028189eefa3
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spelling	oai:doaj.org-article:5ff678ec2ff948c19ad7a028189eefa32021-11-11T19:17:43ZInertial Motion Capture-Based Whole-Body Inverse Dynamics10.3390/s212173531424-8220https://doaj.org/article/5ff678ec2ff948c19ad7a028189eefa32021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7353https://doaj.org/toc/1424-8220Inertial Motion Capture (IMC) systems enable in situ studies of human motion free of the severe constraints imposed by Optical Motion Capture systems. Inverse dynamics can use those motions to estimate forces and moments developing within muscles and joints. We developed an inverse dynamic whole-body model that eliminates the usage of force plates (FPs) and uses motion patterns captured by an IMC system to predict the net forces and moments in 14 major joints. We validated the model by comparing its estimates of Ground Reaction Forces (GRFs) to the ground truth obtained from FPs and comparing predictions of the static model’s net joint moments to those predicted by 3D Static Strength Prediction Program (3DSSPP). The relative root-mean-square error (rRMSE) in the predicted GRF was 6% and the intraclass correlation of the peak values was 0.95, where both values were averaged over the subject population. The rRMSE of the differences between our model’s and 3DSSPP predictions of net L5/S1 and right and left shoulder joints moments were 9.5%, 3.3%, and 5.2%, respectively. We also compared the static and dynamic versions of the model and found that failing to account for body motions can underestimate net joint moments by 90% to 560% of the static estimates.Mohsen M. DiraneyyaJuHyeong RyuEihab Abdel-RahmanCarl T. HaasMDPI AGarticleinertial motion capture (IMC)inverse dynamicsjoint loadergonomicsphysical exposureChemical technologyTP1-1185ENSensors, Vol 21, Iss 7353, p 7353 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	inertial motion capture (IMC) inverse dynamics joint load ergonomics physical exposure Chemical technology TP1-1185
spellingShingle	inertial motion capture (IMC) inverse dynamics joint load ergonomics physical exposure Chemical technology TP1-1185 Mohsen M. Diraneyya JuHyeong Ryu Eihab Abdel-Rahman Carl T. Haas Inertial Motion Capture-Based Whole-Body Inverse Dynamics
description	Inertial Motion Capture (IMC) systems enable in situ studies of human motion free of the severe constraints imposed by Optical Motion Capture systems. Inverse dynamics can use those motions to estimate forces and moments developing within muscles and joints. We developed an inverse dynamic whole-body model that eliminates the usage of force plates (FPs) and uses motion patterns captured by an IMC system to predict the net forces and moments in 14 major joints. We validated the model by comparing its estimates of Ground Reaction Forces (GRFs) to the ground truth obtained from FPs and comparing predictions of the static model’s net joint moments to those predicted by 3D Static Strength Prediction Program (3DSSPP). The relative root-mean-square error (rRMSE) in the predicted GRF was 6% and the intraclass correlation of the peak values was 0.95, where both values were averaged over the subject population. The rRMSE of the differences between our model’s and 3DSSPP predictions of net L5/S1 and right and left shoulder joints moments were 9.5%, 3.3%, and 5.2%, respectively. We also compared the static and dynamic versions of the model and found that failing to account for body motions can underestimate net joint moments by 90% to 560% of the static estimates.
format	article
author	Mohsen M. Diraneyya JuHyeong Ryu Eihab Abdel-Rahman Carl T. Haas
author_facet	Mohsen M. Diraneyya JuHyeong Ryu Eihab Abdel-Rahman Carl T. Haas
author_sort	Mohsen M. Diraneyya
title	Inertial Motion Capture-Based Whole-Body Inverse Dynamics
title_short	Inertial Motion Capture-Based Whole-Body Inverse Dynamics
title_full	Inertial Motion Capture-Based Whole-Body Inverse Dynamics
title_fullStr	Inertial Motion Capture-Based Whole-Body Inverse Dynamics
title_full_unstemmed	Inertial Motion Capture-Based Whole-Body Inverse Dynamics
title_sort	inertial motion capture-based whole-body inverse dynamics
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5ff678ec2ff948c19ad7a028189eefa3
work_keys_str_mv	AT mohsenmdiraneyya inertialmotioncapturebasedwholebodyinversedynamics AT juhyeongryu inertialmotioncapturebasedwholebodyinversedynamics AT eihababdelrahman inertialmotioncapturebasedwholebodyinversedynamics AT carlthaas inertialmotioncapturebasedwholebodyinversedynamics
_version_	1718431586670608384

Inertial Motion Capture-Based Whole-Body Inverse Dynamics

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