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

Lim D, Kim C, Jung H, Jung D, Chun KJ - Clin Interv Aging (2015)

Bottom Line: 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).

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Engineering, Sejong University, Seoul, Republic of Korea.

ABSTRACT
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/m(2)), 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.

No MeSH data available.


Related in: MedlinePlus

Experimental configuration used to generate and characterize the motion of the participants.
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f1-cia-10-1077: Experimental configuration used to generate and characterize the motion of the participants.

Mentions: Figure 1 shows the in-house-manufactured balance training equipment used to induce dynamic movements in the subjects. The balance training equipment could be rotated around various axes (an arbitrary axis) with a range of ±15° of the maximum on the footrest part. Six links were used to connect the footrest part, which could move ±0.1 m in the vertical direction. The tests were designed to control the basal plane in an arbitrary direction. For safety reasons, the range of rotation angles was limited to ±9° around the axis of rotation. Subjects were allowed to move dynamically within a range that allowed them to maintain their balance. Tests were repeated three times per subject to minimize measurement errors, and a 10-minute rest period introduced between tests was to minimize the effects of fatigue.


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

Lim D, Kim C, Jung H, Jung D, Chun KJ - Clin Interv Aging (2015)

Experimental configuration used to generate and characterize the motion of the participants.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493972&req=5

f1-cia-10-1077: Experimental configuration used to generate and characterize the motion of the participants.
Mentions: Figure 1 shows the in-house-manufactured balance training equipment used to induce dynamic movements in the subjects. The balance training equipment could be rotated around various axes (an arbitrary axis) with a range of ±15° of the maximum on the footrest part. Six links were used to connect the footrest part, which could move ±0.1 m in the vertical direction. The tests were designed to control the basal plane in an arbitrary direction. For safety reasons, the range of rotation angles was limited to ±9° around the axis of rotation. Subjects were allowed to move dynamically within a range that allowed them to maintain their balance. Tests were repeated three times per subject to minimize measurement errors, and a 10-minute rest period introduced between tests was to minimize the effects of fatigue.

Bottom Line: 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).

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Engineering, Sejong University, Seoul, Republic of Korea.

ABSTRACT
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/m(2)), 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.

No MeSH data available.


Related in: MedlinePlus