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Research Papers

Analysis of Recoverable Falls Via microsoft kinect: Identification of Third-Order Ankle Dynamics

[+] Author and Article Information
Mauricio E. Segura

Facultad de Ingeniería,
Universidad Autónoma de San Luis Potosí,
San Luis Potosí 78290, México
e-mail: mauricio.segura@uaslp.mx

Enrique Coronado

Facultad de Ingeniería,
Universidad Autónoma de San Luis Potosí,
San Luis Potosí 78290, México
e-mail: luis.coronado@uaslp.mx

Mauro Maya

Facultad de Ingeniería,
Universidad Autónoma de San Luis Potosí,
San Luis Potosí 78290, México
e-mail: mauro.maya@uaslp.mx

Antonio Cardenas

Facultad de Ingeniería,
Universidad Autónoma de San Luis Potosí,
San Luis Potosí 78290, México
e-mail: antonio.cardenas@uaslp.mx

Davide Piovesan

Biomedical Engineering Program,
Gannon University,
Erie, PA 16541
e-mail: piovesan001@gannon.ed

1Corresponding author.

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.

J. Dyn. Sys., Meas., Control 138(9), 091006 (Jun 02, 2016) (10 pages) Paper No: DS-15-1488; doi: 10.1115/1.4032878 History: Received October 05, 2015; Revised February 23, 2016

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.

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Figures

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Fig. 1

Graphical representation of a (a) second- and (b) third-order muscle–tendon system

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Fig. 2

Calibration of the visual system

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Fig. 3

Locations of the visual system, the visual markers, and kinect

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Fig. 4

State estimates of GA+EKF algorithm on synthetic data

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Fig. 5

Comparison between the data obtained by mk and the EKF state estimation for all the subjects

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Fig. 6

Comparison between the data obtained by mk and the EKF state estimation for all the subjects

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Fig. 7

Comparison between the data obtained by mk (solid) and CVS (dashed). The part of the graph to the right of the dashed line represents the recoil phase as described in Ref. [18] and it has been used to identify the system's parameters.

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Fig. 8

Experimental results for camera versus kinect v2: (a) camera data and (b) kinect data

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