Identification of Time-Varying Ankle Joint Impedance During Periodic Torque Experiments Using Kernel-Based Regression

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

Joint impedance is a common way of representing human joint dynamics. Since ankle joint impedance varies within the gait cycle, time-varying system identification techniques can 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 publicationConverging Clinical and Engineering Research on Neurorehabilitation IV
Subtitle of host publicationProceedings of the 5th International Conference on Neurorehabilitation (ICNR2020), October 13–16, 2020
EditorsDiego Torricelli, Metin Akay, Jose L. Pons
PublisherSpringer Science + Business Media
Pages495-499
Number of pages5
ISBN (Electronic)978-3-030-70316-5
ISBN (Print)978-3-030-70315-8, 978-3-030-70318-9
DOIs
Publication statusPublished - 2022
Event5th International Conference on NeuroRehabilitation, ICNR 2020 - Virtual Event
Duration: 13 Oct 202016 Oct 2020
Conference number: 5

Publication series

NameBiosystems and Biorobotics
Volume28
ISSN (Print)2195-3562
ISSN (Electronic)2195-3570

Conference

Conference5th International Conference on NeuroRehabilitation, ICNR 2020
Abbreviated titleICNR 2020
CityVirtual Event
Period13/10/2016/10/20

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