Open Access

ARTICLE

Design of Human Adaptive Mechatronics Controller for Upper Limb Motion Intention Prediction

by R. Joshua Samuel Raj^1,*, J. Prince Antony Joel², Salem Alelyani³, Mohammed Saleh Alsaqer³, C. Anand Deva Durai⁴

1 Department of Information Science & Engineering, CMR Institute of Technology, Bengaluru, India
2 Department of Mechatronics Engineering, Rajas Engineering College, Tirunelveli, India
3 Center for Artificial Intelligence, King Khalid University, Abha, Kingdom of Saudi Arabia
4 College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia

* Corresponding Author: R. Joshua Samuel Raj. Email:

Computers, Materials & Continua 2022, 71(1), 1171-1188. https://doi.org/10.32604/cmc.2022.021667

Received 09 July 2021; Accepted 17 August 2021; Issue published 03 November 2021

Abstract

Human Adaptive Mechatronics (HAM) includes human and computer system in a closed loop. Elderly person with disabilities, normally carry out their daily routines with some assistance to move their limbs. With the short fall of human care takers, mechatronics devices are used with the likes of exoskeleton and exosuits to assist them. The rehabilitation and occupational therapy equipments utilize the electromyography (EMG) signals to measure the muscle activity potential. This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system. Limb characteristics extraction from EMG signal and prediction of optimal controller parameters are modeled. Time and frequency based approach of EMG signal are considered for feature extraction. The models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are validated. Based on the extracted features, optimal parameters are selected by Modified Lion Optimization (MLO) for controlling the HAM system. Finally, supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network (SVNN). This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb movements. The proposed model of human adaptive controller predicts the limb movement by 96% accuracy.

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Keywords

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