Analysis of Recoverable Falls Via microsoft kinect: Identification of Third-Order Ankle Dynamics
related by: authorship_27284
freetext keyword: 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
doi: 10.1115/1.4032878
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related by: authorship_27287
related by: authorship_27286
crisid: 4644
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has publication venue: Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME
type: Document
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origen: scopus
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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.
related by: authorship_27285
type: Continuant
type: Article
issue: 9
artnum: 91006
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&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. Copyright © 2016 by ASME.
volume: 138
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related by: authorship_27288
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RDF description of Analysis of Recoverable Falls Via microsoft kinect: Identification of Third-Order Ankle Dynamics - http://orbis.uaslp.mx/vivo/individual/scopus4643
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date: 2026-08-27T06:36:00
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Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME
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publication venue for: Analysis of Recoverable Falls Via microsoft kinect: Identification of Third-Order Ankle Dynamics
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