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&R) 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.
Analysis of Recoverable Falls Via microsoft kinect: Identification of Third-Order Ankle Dynamics
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 5, 2015; final manuscript received February 23, 2016; published online June 2, 2016. Assoc. Editor: Davide Spinello.
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Segura, M. E., Coronado, E., Maya, M., Cardenas, A., and Piovesan, D. (June 2, 2016). "Analysis of Recoverable Falls Via microsoft kinect: Identification of Third-Order Ankle Dynamics." ASME. J. Dyn. Sys., Meas., Control. September 2016; 138(9): 091006. https://doi.org/10.1115/1.4032878
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