Accuracy of image data stream of a markerless motion capture system in determining the local dynamic stability and joint kinematics of human gait. (7th May 2020)
- Record Type:
- Journal Article
- Title:
- Accuracy of image data stream of a markerless motion capture system in determining the local dynamic stability and joint kinematics of human gait. (7th May 2020)
- Main Title:
- Accuracy of image data stream of a markerless motion capture system in determining the local dynamic stability and joint kinematics of human gait
- Authors:
- Chakraborty, Saikat
Nandy, Anup
Yamaguchi, Takazumi
Bonnet, Vincent
Venture, Gentiane - Abstract:
- Abstract: Assessment of gait parameters is commonly performed through the high-end motion tracking systems, which limits the measurement to sophisticated laboratory settings due to its excessive cost. Recently, Microsoft Kinect (v2) sensor has become popular in clinical gait analysis due to its low-cost. But, determining the accuracy of its RGB-D image data stream in measuring the joint kinematics and local dynamic stability remains an unsolved problem. This study examined the suitability of Kinect(v2) RGB-D image data stream in assessing those gait parameters. Fifteen healthy participants walked on a treadmill during which lower body kinematics were measured by a Kinect(v2) sensor and a optophotogrametric tracking system, simultaneously. Extended Kalman filter was used to extract the lower extremity joint angles from Kinect, while inverse kinematics was used for the gold standard system. For both systems, local dynamic stability was assessed using maximal Lyapunov exponent. Sprague's validation metrics, root mean square error (RMSE) and normalized RMSE were computed to confirm the difference between the joint angles time series of the two systems while relative agreement between them was investigated through Pearson's correlation coefficient ( p r ). Fisher's Exact Test was performed on maximal Lyapunov exponent to investigate the data independence while reliability was assessed using intraclass correlation coefficients. This study concludes that the RGB-D data stream ofAbstract: Assessment of gait parameters is commonly performed through the high-end motion tracking systems, which limits the measurement to sophisticated laboratory settings due to its excessive cost. Recently, Microsoft Kinect (v2) sensor has become popular in clinical gait analysis due to its low-cost. But, determining the accuracy of its RGB-D image data stream in measuring the joint kinematics and local dynamic stability remains an unsolved problem. This study examined the suitability of Kinect(v2) RGB-D image data stream in assessing those gait parameters. Fifteen healthy participants walked on a treadmill during which lower body kinematics were measured by a Kinect(v2) sensor and a optophotogrametric tracking system, simultaneously. Extended Kalman filter was used to extract the lower extremity joint angles from Kinect, while inverse kinematics was used for the gold standard system. For both systems, local dynamic stability was assessed using maximal Lyapunov exponent. Sprague's validation metrics, root mean square error (RMSE) and normalized RMSE were computed to confirm the difference between the joint angles time series of the two systems while relative agreement between them was investigated through Pearson's correlation coefficient ( p r ). Fisher's Exact Test was performed on maximal Lyapunov exponent to investigate the data independence while reliability was assessed using intraclass correlation coefficients. This study concludes that the RGB-D data stream of Kinect sensor is efficient in estimating joint kinematics, but not suitable for measuring the local dynamic stability. … (more)
- Is Part Of:
- Journal of biomechanics. Volume 104(2020)
- Journal:
- Journal of biomechanics
- Issue:
- Volume 104(2020)
- Issue Display:
- Volume 104, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 104
- Issue:
- 2020
- Issue Sort Value:
- 2020-0104-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-05-07
- Subjects:
- Kinect v2 -- Gait -- Extended Kalman filter -- Local dynamic stability -- Maximal Lyapunov exponent
Animal mechanics -- Periodicals
Biomechanics -- Periodicals
Biomechanics -- Periodicals
Mécanique animale -- Périodiques
Biomécanique -- Périodiques
Electronic journals
571.4305 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00219290 ↗
http://www.clinicalkey.com/dura/browse/journalIssue/00219290 ↗
http://www.clinicalkey.com.au/dura/browse/journalIssue/00219290 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.jbiomech.2020.109718 ↗
- Languages:
- English
- ISSNs:
- 0021-9290
- Deposit Type:
- Legaldeposit
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- British Library DSC - 4953.600000
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