Bamdad, Mahdi and Mokri, Chiako and Abolghasemi, Vahid (2022) Joint mechanical properties estimation with a novel EMG-based knee rehabilitation robot: A machine learning approach. Medical Engineering and Physics, 110. p. 103933. DOI https://doi.org/10.1016/j.medengphy.2022.103933
Bamdad, Mahdi and Mokri, Chiako and Abolghasemi, Vahid (2022) Joint mechanical properties estimation with a novel EMG-based knee rehabilitation robot: A machine learning approach. Medical Engineering and Physics, 110. p. 103933. DOI https://doi.org/10.1016/j.medengphy.2022.103933
Bamdad, Mahdi and Mokri, Chiako and Abolghasemi, Vahid (2022) Joint mechanical properties estimation with a novel EMG-based knee rehabilitation robot: A machine learning approach. Medical Engineering and Physics, 110. p. 103933. DOI https://doi.org/10.1016/j.medengphy.2022.103933
Abstract
Joint dynamic properties play essential roles in a wide range of biomechanical movement control. This paper develops a device with a novel mechatronic design to apply small-amplitude perturbations to the human knee. Surface Electromyography is employed to record such information; at the same time, force and position sensors collect measurements to be sent to identify human joint dynamics. For classification and estimation of force, support vector machine and support vector regression techniques are applied, respectively. We devise a genetic algorithm for parameter optimization and feature extraction within the proposed methods to improve the estimation accuracy. These are then analyzed and compared to the output of our estimation model to provide a reliable comparison. Our extensive experimental results reveal a high estimation accuracy for lower limb muscles to regulate robot impedance parameters. Although the identification method sounds similar to traditional ones, knee joint properties can be estimated by the machine learning approach from the surface Electromyography without perturbations.
Item Type: | Article |
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Uncontrolled Keywords: | Knee joint model; Impedance; Rehabilitation robot; Electromyography; Support vector machine |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 10 Feb 2023 21:13 |
Last Modified: | 30 Oct 2024 15:50 |
URI: | http://repository.essex.ac.uk/id/eprint/34659 |
Available files
Filename: Accepted.pdf
Licence: Creative Commons: Attribution-Noncommercial 4.0