Identification of Time-varying Ankle Joint Impedance during Periodic Torque Experiments using Kernel-Based Regression

Gaia Cavallo, Christopher P. Cop, Massimo Sartori, Alfred C. Schouten, John Lataire

Research output: Chapter in Book/Report/Conference proceedingConference paper

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Abstract

Joint impedance represents the human joint dynamics. Since ankle joint impedance varies within the gait cycle, time-varying system identification techniques should be used to estimate it. Commonly, time-varying system identification techniques assume repeatably of joint impedance over cyclic
motions, without taking into consideration the inherent variability of human behavior. In this paper, a method that assumes smooth, cyclic joint impedance, yet allows for cycle-to-cycle variability, is proposed. The method was tested on isometric, cyclic experimental data from the ankle under conditions with a time variation comparable to the expected one during the gait cycle. The estimated model could describe the data with high accuracy (VAF of 94.96 %) and retrieve realistic inertia, damping and stiffness parameters. The results provide
motivation to further apply the method on experiments under dynamic conditions and to employ the proposed method as a tool for investigating the human joint dynamics during cyclic movements.
Original languageEnglish
Title of host publication5th International Conference on NeuroRehabilitation (ICNR2020)
PublisherSpringer
Pages495-499
Number of pages5
ISBN (Electronic)978-3-030-70316-5
ISBN (Print)978-3-030-70315-8
Publication statusPublished - 2021
Event5th International Conference on NeuroRehabilitation - Virtual format
Duration: 13 Oct 202016 Oct 2020
http://www.icnr2020.org/

Conference

Conference5th International Conference on NeuroRehabilitation
Abbreviated titleICNR2020
Period13/10/2016/10/20
Internet address

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