Citation

Kamran, Muhammad and Khan, Talha Ahmed and Iftikhar, Umar and Rizvi, Safdar A. and Tanoli, Irfan and Kadir, Kushsairy (2023) Comparative Analysis for Machine-Learning-Based Optimal Control of Upper Extremity Rehabilitation Robots. In: The 8th International Electrical Engineering Conference, 25–26 August 2023, Karachi, Pakistan.

Text 5.pdf - Published Version Restricted to Repository staff only Download (223kB)

Abstract

It has been observed from many pieces of research and through applications that robotic movements using human interaction are considered dangerous, tiresome and require extraordinary precision and smooth control. Specifically, medical and healthcare applications have been the highest priority in recent years. The concept of rehabilitation using robotics was introduced during the 1980s with the motive of freeing therapists from repetitive work while treating an increasing elderly population requiring physiotherapy. Furthermore, the consistency of the robot’s operation and the volume of repetitions has increased. They can assist therapists in performing tedious tasks and let them concentrate on several patients simultaneously. Several types of rehabilitation robot devices have been produced in recent years with different modes of training and control strategies using various control algorithms. In this research paper, a comprehensive overview of rehabilitation in relation to robotics is presented. The main aim is to determine robust controlling optimization for the smooth control of robotic movement, as these movements require a lot of precision and accuracy. The analysis showed that M-PSO was found to be very effective and robust in finding the best optimal values, as the Modified PSO achieved the minimum root mean square value and a best fit of 98.7.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	upper extremity; rehabilitation; optimization; smooth control
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	23 Feb 2024 03:46
Last Modified:	23 Feb 2024 03:46
URII:	http://shdl.mmu.edu.my/id/eprint/12131

Downloads

Edit (login required)