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: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
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Dove Medical Press
2015
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/8622ec28846f41b78b3d94b8418c74bd |
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| Sumario: | 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 |
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