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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

Changes in the center of body mass (COM).Notes: (A) In the medial–lateral direction (x-axis). (B) In the anterior–posterior direction (y-axis). (C) In the cranial–caudal direction (z-axis). (D) A 3D representation of the alteration of COM.
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f2-cia-10-1077: Changes in the center of body mass (COM).Notes: (A) In the medial–lateral direction (x-axis). (B) In the anterior–posterior direction (y-axis). (C) In the cranial–caudal direction (z-axis). (D) A 3D representation of the alteration of COM.

Mentions: Figure 2 shows changes in the COM determined using the Kinect system and the 3D infrared camera system during movements of the subjects, and Table 1 lists a summary of these data. The measured changes in the COM of the subjects that were required to maintain balance were almost identical using the two systems; ie, Px=0.22, Py=0.30, and Pz=0.13. The differences in the COM measured using the Kinect system were 135.07±22.06 mm in the medial–lateral axis (x-axis), 98.02±18.27 mm in the anterior–posterior axis (y-axis), and 67.49±32.4 mm in the cranial–caudal axis (z-axis); using the infrared camera 3D motion capture system, we found 118.82±21.22 mm in the x-axis, 118.81±42.46 mm in the y-axis, and 43.84±12.26 mm in the z-axis. The measurements using the two systems were strongly correlated, and we found γx=0.61, γy=0.59, and γz=0.66, which shows that the Kinect system provided a similar characterization of the movements of the subjects to the 3D infrared camera system.


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)

Changes in the center of body mass (COM).Notes: (A) In the medial–lateral direction (x-axis). (B) In the anterior–posterior direction (y-axis). (C) In the cranial–caudal direction (z-axis). (D) A 3D representation of the alteration of COM.
© Copyright Policy
Related In: Results  -  Collection

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

f2-cia-10-1077: Changes in the center of body mass (COM).Notes: (A) In the medial–lateral direction (x-axis). (B) In the anterior–posterior direction (y-axis). (C) In the cranial–caudal direction (z-axis). (D) A 3D representation of the alteration of COM.
Mentions: Figure 2 shows changes in the COM determined using the Kinect system and the 3D infrared camera system during movements of the subjects, and Table 1 lists a summary of these data. The measured changes in the COM of the subjects that were required to maintain balance were almost identical using the two systems; ie, Px=0.22, Py=0.30, and Pz=0.13. The differences in the COM measured using the Kinect system were 135.07±22.06 mm in the medial–lateral axis (x-axis), 98.02±18.27 mm in the anterior–posterior axis (y-axis), and 67.49±32.4 mm in the cranial–caudal axis (z-axis); using the infrared camera 3D motion capture system, we found 118.82±21.22 mm in the x-axis, 118.81±42.46 mm in the y-axis, and 43.84±12.26 mm in the z-axis. The measurements using the two systems were strongly correlated, and we found γx=0.61, γy=0.59, and γz=0.66, which shows that the Kinect system provided a similar characterization of the movements of the subjects to the 3D infrared camera system.

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