Validation of online intrinsic and reflexive joint impedance estimates using correlation with EMG measurements

Conference paper (2018)

Authors

Ronald C. Van 't Veld University of Twente
Alfred C. Schouten University of Twente, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
AC Schouten University of Twente, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
A.C. Schouten University of Twente, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Herman van der Kooij University of Twente, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Herman van der van der Kooij University of Twente, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
H. Kooij University of Twente, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
H. van der Kooij University of Twente, Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Edwin H.F. Van Asseldonk University of Twente
Research Group
Biomechatronics & Human-Machine Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2018 Ronald C. Van'T Veld, A.C. Schouten, H. van der Kooij, Edwin H.F. Van Asseldonk
DOI: https://doi.org/10.1109/BIOROB.2018.8488123
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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

Biofeedback of online system identification estimates of intrinsic and reflexive joint impedance can be used by able-bodied subjects to voluntarily modulate their reflexive impedance independent of the intrinsic contribution. Similar to EMG-based paradigms, this could potentially be used to reduce muscle hyperreflexia in people with spasticity by facilitating spinal neuroplasticity. However, it remains unanswered if spastic participants are able to use this specific feedback to modulate their reflexes. We show, while subjects were free to co-contract, that the system identification measures have a large linear association with independently measured and processed EMG measures. The impedance estimates were obtained using an existing algorithm with incremental improvements to increase general applicability and decrease bias on the identified parameters in both simulation an experimental data. The correlation with EMG-based measures demonstrates the validity of the use of joint impedance measures within a training paradigm to reduce hyperreflexia. This could potentially improve participant comfort, increase applicability across joints, target hyperreflexia at joint level and generate faster training effects.

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