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

Book chapter (2022)

Authors

Gaia Cavallo Vrije Universiteit Brussel
Christopher P. Cop University of Twente
M. Sartori University of Twente
A.C. Schouten University of Twente, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
John Lataire Vrije Universiteit Brussel
Research Group
Biomechatronics & Human-Machine Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Gaia Cavallo, Christopher P. Cop, M. Sartori, A.C. Schouten, John Lataire
DOI: https://doi.org/10.1007/978-3-030-70316-5_79
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Biomechanical Engineering

Research Group

Biomechatronics & Human-Machine Control

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.

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