abstract

• This work combines the kinematics estimate of human standing with a hybrid identification algorithm to identify a set of ankle dynamics mechanical parameters. We used the hold and release (H%26R) experimental paradigm to model a set of recoverable falls on a population of unimpaired adults. Body kinematics was acquired with a microsoft kinect (mk) version 2 after benchmarking its position accuracy to a camera-based vision system (CVS). The system identification algorithm, combining an extended Kalman filter (EKF) and a genetic algorithm (GA), allowed to identify the effect of tendon and muscle stiffness at the ankle joint, separately. This work highlights that, when associated to soft-computing techniques, affordable tracking devices developed for the gaming industry can be used for the reliable assessment of neuromechanical parameters in clinical settings. Copyright © 2016 by ASME.

authors

• Coronado E.
• Cárdenas Galindo Juan Antonio
• Maya Méndez Mauro Eduardo
• Piovesan D.
• Segura M.E.

publication date

• 2016-01-01

published in

• Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME  Journal

keywords

• Extended Kalman filters; Genetic algorithms; Kinematics; Soft computing; Camera based vision system; Clinical settings; Hybrid identification algorithms; Mechanical parameters; Position accuracy; Reliable assessment; Softcomputing techniques; System identification algorithms; Parameter estimation

Digital Object Identifier (DOI)

• 10.1115/1.4032878

start page

end page

volume

• 138

issue

• 9