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Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques.

Mokri, Chiako and Bamdad, Mahdi and Abolghasemi, Vahid (2022) 'Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques.' Medical and Biological Engineering and Computing, 60 (3). pp. 683-699. ISSN 0140-0118

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Abstract

The main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length. Graphical Abstract.

Item Type: Article
Uncontrolled Keywords: Rehabilitation robotics; Electromyography; Support vector machine; Support vector regression; Genetic algorithm; Random forest
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 11 Feb 2022 16:19
Last Modified: 21 Feb 2022 12:07
URI: http://repository.essex.ac.uk/id/eprint/32282

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