Revealing time-varying joint impedance with kernel-based regression and nonparametric decomposition

Journal article (2018)

Authors

M.L. van de Ruit Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Gaia Cavallo Vrije Universiteit Brussel
John Lataire Vrije Universiteit Brussel
F.C.T. van der Helm Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
W. Mugge Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
J.W. van Wingerden Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
A.C. Schouten Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Research Group
Biomechatronics & Human-Machine Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2018 M.L. van de Ruit, Gaia Cavallo, John Lataire, F.C.T. van der Helm, W. Mugge, J.W. van Wingerden, A.C. Schouten
DOI: https://doi.org/10.1109/TCST.2018.2881664
Joint impedance Human motor control System identification (SI) Time-varying systems.
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Published Date

2018

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Biomechanical Engineering

Research Group

Biomechatronics & Human-Machine Control

Abstract

During movements, humans continuously regulate their joint impedance to
minimize control effort and optimize performance. Joint impedance
describes the relationship between a joint's position and torque acting
around the joint. Joint impedance varies with joint angle and muscle
activation and differs from trial-to-trial due to inherent variability
in the human control system. In this paper, a dedicated time-varying
system identification (SI) framework is developed involving a
parametric, kernel-based regression, and nonparametric, “skirt
decomposition,” SI method to monitor the time-varying joint impedance
during a force task. Identification was performed on single trials and
the estimators included little a priori assumptions regarding the
underlying time-varying joint mechanics. During the experiments, six
(human) participants used flexion of the wrist to apply a slow
sinusoidal torque to the handle of a robotic manipulator, while
receiving small position perturbations. Both methods revealed that the
sinusoidal change in joint torque by activation of the wrist flexor
muscles resulted in a sinusoidal time-varying joint stiffness and
resonance frequency. A third-order differential equation allowed the
parametric kernel-based estimator to explain on average 76% of the
variance (range 52%-90%). The nonparametric skirt decomposition method
could explain on average 84% of the variance (range 66%-91%). This paper
presents a novel framework for identification of time-varying joint
impedance by making use of linear time-varying models based on a single
trial of data.