Use of the Microsoft Kinect system to characterize balance ability during balance training

Dohyung Lim,1 ChoongYeon Kim,2 HoHyun Jung,1 Dukyoung Jung,3 Keyoung Jin Chun21Department of Mechanical Engineering, Sejong University, Seoul, Republic of Korea; 2Advanced Biomedical Engineering Lab, Korea Institute of Industrial Technology, Cheonan, Republic of Korea; 3R&D Team, Senior Prod...

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Autores principales:	Lim DH, Kim CY, Jung HH, Jung DY, Chun KJ
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2015
Materias:	Balance Ability Balance Training Motion Capture System Kinect System Fall Prevention Geriatrics RC952-954.6
Acceso en línea:	https://doaj.org/article/8622ec28846f41b78b3d94b8418c74bd
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spelling	oai:doaj.org-article:8622ec28846f41b78b3d94b8418c74bd2021-12-02T09:03:25ZUse of the Microsoft Kinect system to characterize balance ability during balance training1178-1998https://doaj.org/article/8622ec28846f41b78b3d94b8418c74bd2015-06-01T00:00:00Zhttps://www.dovepress.com/use-of-the-microsoft-kinect-system-to-characterize-balance-ability-dur-peer-reviewed-article-CIAhttps://doaj.org/toc/1178-1998Dohyung Lim,1 ChoongYeon Kim,2 HoHyun Jung,1 Dukyoung Jung,3 Keyoung Jin Chun21Department of Mechanical Engineering, Sejong University, Seoul, Republic of Korea; 2Advanced Biomedical Engineering Lab, Korea Institute of Industrial Technology, Cheonan, Republic of Korea; 3R&D Team, Senior Products Industrial Center, Busan, Republic of KoreaAbstract: The risk of falling increases significantly in the elderly because of deterioration of the neural musculature regulatory mechanisms. Several studies have investigated methods of preventing falling using real-time systems to evaluate balance; however, it is difficult to monitor the results of such characterizations in real time. Herein, we describe the use of Microsoft’s Kinect depth sensor system to evaluate balance in real time. Six healthy male adults (25.5±1.8 years, 173.9±6.4 cm, 71.4±6.5 kg, and 23.6±2.4 kg/m2), with normal balance abilities and with no musculoskeletal disorders, were selected to participate in the experiment. Movements of the participants were induced by controlling the base plane of the balance training equipment in various directions. The dynamic motion of the subjects was measured using two Kinect depth sensor systems and a three-dimensional motion capture system with eight infrared cameras. The two systems yielded similar results for changes in the center of body mass (P>0.05) with a large Pearson’s correlation coefficient of γ>0.60. The results for the two systems showed similarity in the mean lower-limb joint angle with flexion–extension movements, and these values were highly correlated (hip joint: within approximately 4.6°; knee joint: within approximately 8.4°) (0.40<γ<0.74) (P>0.05). Large differences with a low correlation were, however, observed for the lower-limb joint angle in relation to abduction–adduction and internal–external rotation motion (γ<0.40) (P<0.05). These findings show that clinical and dynamic accuracy can be achieved using the Kinect system in balance training by measuring changes in the center of body mass and flexion–extension movements of the lower limbs, but not abduction–adduction and internal–external rotation.Keywords: balance ability, balance training, motion capture system, Kinect system, fall preventionLim DHKim CYJung HHJung DYChun KJDove Medical PressarticleBalance AbilityBalance TrainingMotion Capture SystemKinect SystemFall PreventionGeriatricsRC952-954.6ENClinical Interventions in Aging, Vol Volume 10, Pp 1077-1083 (2015)
institution	DOAJ
collection	DOAJ
language	EN
topic	Balance Ability Balance Training Motion Capture System Kinect System Fall Prevention Geriatrics RC952-954.6
spellingShingle	Balance Ability Balance Training Motion Capture System Kinect System Fall Prevention Geriatrics RC952-954.6 Lim DH Kim CY Jung HH Jung DY Chun KJ Use of the Microsoft Kinect system to characterize balance ability during balance training
description	Dohyung Lim,1 ChoongYeon Kim,2 HoHyun Jung,1 Dukyoung Jung,3 Keyoung Jin Chun21Department of Mechanical Engineering, Sejong University, Seoul, Republic of Korea; 2Advanced Biomedical Engineering Lab, Korea Institute of Industrial Technology, Cheonan, Republic of Korea; 3R&D Team, Senior Products Industrial Center, Busan, Republic of KoreaAbstract: The risk of falling increases significantly in the elderly because of deterioration of the neural musculature regulatory mechanisms. Several studies have investigated methods of preventing falling using real-time systems to evaluate balance; however, it is difficult to monitor the results of such characterizations in real time. Herein, we describe the use of Microsoft’s Kinect depth sensor system to evaluate balance in real time. Six healthy male adults (25.5±1.8 years, 173.9±6.4 cm, 71.4±6.5 kg, and 23.6±2.4 kg/m2), with normal balance abilities and with no musculoskeletal disorders, were selected to participate in the experiment. Movements of the participants were induced by controlling the base plane of the balance training equipment in various directions. The dynamic motion of the subjects was measured using two Kinect depth sensor systems and a three-dimensional motion capture system with eight infrared cameras. The two systems yielded similar results for changes in the center of body mass (P>0.05) with a large Pearson’s correlation coefficient of γ>0.60. The results for the two systems showed similarity in the mean lower-limb joint angle with flexion–extension movements, and these values were highly correlated (hip joint: within approximately 4.6°; knee joint: within approximately 8.4°) (0.40<γ<0.74) (P>0.05). Large differences with a low correlation were, however, observed for the lower-limb joint angle in relation to abduction–adduction and internal–external rotation motion (γ<0.40) (P<0.05). These findings show that clinical and dynamic accuracy can be achieved using the Kinect system in balance training by measuring changes in the center of body mass and flexion–extension movements of the lower limbs, but not abduction–adduction and internal–external rotation.Keywords: balance ability, balance training, motion capture system, Kinect system, fall prevention
format	article
author	Lim DH Kim CY Jung HH Jung DY Chun KJ
author_facet	Lim DH Kim CY Jung HH Jung DY Chun KJ
author_sort	Lim DH
title	Use of the Microsoft Kinect system to characterize balance ability during balance training
title_short	Use of the Microsoft Kinect system to characterize balance ability during balance training
title_full	Use of the Microsoft Kinect system to characterize balance ability during balance training
title_fullStr	Use of the Microsoft Kinect system to characterize balance ability during balance training
title_full_unstemmed	Use of the Microsoft Kinect system to characterize balance ability during balance training
title_sort	use of the microsoft kinect system to characterize balance ability during balance training
publisher	Dove Medical Press
publishDate	2015
url	https://doaj.org/article/8622ec28846f41b78b3d94b8418c74bd
work_keys_str_mv	AT limdh useofthemicrosoftkinectsystemtocharacterizebalanceabilityduringbalancetraining AT kimcy useofthemicrosoftkinectsystemtocharacterizebalanceabilityduringbalancetraining AT junghh useofthemicrosoftkinectsystemtocharacterizebalanceabilityduringbalancetraining AT jungdy useofthemicrosoftkinectsystemtocharacterizebalanceabilityduringbalancetraining AT chunkj useofthemicrosoftkinectsystemtocharacterizebalanceabilityduringbalancetraining
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Use of the Microsoft Kinect system to characterize balance ability during balance training

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