MODEL-BASED EVALUATION OF THE CONTROL STRATEGIES OF A HAND REHABILITATION ROBOT BASED ON MOTOR LEARNING PRINCIPLES

ONUR CAN YAĞMUR

MODEL-BASED EVALUATION OF THE CONTROL STRATEGIES OF A HAND REHABILITATION ROBOT BASED ON MOTOR LEARNING PRINCIPLES

Thesis Type: Postgraduate

Institution Of The Thesis: Middle East Technical University, Graduate School of Natural and Applied Sciences, MAKİNE MÜHENDİSLİĞİ ANABİLİM DALI, Turkey

Approval Date: 2022

Thesis Language: English

Student: ONUR CAN YAĞMUR

Principal Supervisor (For Co-Supervisor Theses): Ali Emre Turgut

Co-Supervisor: Kutluk Bilge Arikan

Open Archive Collection: AVESIS Open Access Collection

Abstract:

Stroke is an important health problem that occurs after blockage or bleeding in the vessels feeding the brain. It is one of the leading causes of death in the world. Patients with surviving hemiplegia often have a loss or decrease in voluntary movement of the right or left side of the body. The disease reduces the quality of life of patients with its spasticity and limits their independence; This situation poses an important problem for patients, their relatives, and the whole society. This thesis aims to develop control strategies for a robotic hand rehabilitation exoskeleton, which focuses on recovery based on motor learning principles, for patients with hemiplegia. The targeted system is designed to support/encourage motor learning. In this way, it is aimed to increase the effectiveness of therapy. Simulink/ MATLAB is used for both the simulations of the mechanism and the patient. To maximize motor learning in the therapy process, it has been suggested that the control system should be developed under assist as needed approach. Detailed mathematical models that describe the resistance torques in the joints include the mechanics of contact and interaction with the object squeezed during the pinching action. Reinforcement learning is used to model the cortical reorganization and to simulate the patient in the therapy process. Kinematic and admittance control strategies are used for rehabilitation simulations and slacking hypothesis is investigated. Admittance controller results in less slacking compared to kinematic controller. Moreover, the effect of spring stiffness in the admittance controller over learning is investigated.