Vol.71, No.1, 2022, pp.1171-1188, doi:10.32604/cmc.2022.021667
OPEN ACCESS
ARTICLE
Design of Human Adaptive Mechatronics Controller for Upper Limb Motion Intention Prediction
  • R. Joshua Samuel Raj1,*, 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:
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.
Keywords
Exoskeleton; electromyography (emg); human adaptive mechatronics; occupational therapy; motion prediction; machine learning
Cite This Article
Joshua, R., Prince, J., Alelyani, S., Alsaqer, M. S., Anand, C. (2022). Design of Human Adaptive Mechatronics Controller for Upper Limb Motion Intention Prediction. CMC-Computers, Materials & Continua, 71(1), 1171–1188.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.