abstract

• This paper presents an affordable platform to estimate human ankle mechanical impedance. This platform uses Microsoft Kinect version 2 as the motion capture system of choice and a hybrid algorithm to estimate the biomechanical parameters. The algorithm is based on the combination of an Extended Kalman filter and a Genetic Algorithms. The information provided by Kinect together with the ankle biomechanical parameters can be utilized to estimate the dynamic behavior of the recovery from falls. To prove the precision of the 3D measurements obtained with Kinect a comparison with a visual system, based on two industrial cameras was performed. Both systems were calibrated tracking the end-effector position of an industrial robot. The hold and release (H%26R) experimental paradigm was used to estimate the ankle mechanics on seven subjects. The results show that Kinect v2 is a reliable motion capture device to study the neuro-mechanical response of recovering from falling. © 2015 IEEE.
• This paper presents an affordable platform to estimate human ankle mechanical impedance. This platform uses Microsoft Kinect version 2 as the motion capture system of choice and a hybrid algorithm to estimate the biomechanical parameters. The algorithm is based on the combination of an Extended Kalman filter and a Genetic Algorithms. The information provided by Kinect together with the ankle biomechanical parameters can be utilized to estimate the dynamic behavior of the recovery from falls. To prove the precision of the 3D measurements obtained with Kinect a comparison with a visual system, based on two industrial cameras was performed. Both systems were calibrated tracking the end-effector position of an industrial robot. The hold and release (H&R) experimental paradigm was used to estimate the ankle mechanics on seven subjects. The results show that Kinect v2 is a reliable motion capture device to study the neuro-mechanical response of recovering from falling. © 2015 IEEE.

authors

• Coronado E.
• Cárdenas Galindo Juan Antonio
• Piovesan D.
• Segura M.E.

publication date

• 2016-01-01

funding provided via

• / 103.5/13/9353  Grant
• Consejo Nacional de Ciencia y Tecnología, CONACYT: 246 573  Grant

published in

• 2015 IEEE Signal Processing in Medicine and Biology Symposium - Proceedings  Proceedings

keywords

• Algorithms; Biomechanics; End effectors; Genetic algorithms; 3-D measurement; Biomechanical parameters; Dynamic behaviors; Hybrid algorithms; Mechanical impedances; Mechanical response; Microsoft kinect; Motion capture system; Signal processing

Digital Object Identifier (DOI)

• 10.1109/SPMB.2015.7405424

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