Design of an Intelligent Control Architecture for Rehabilitation Robotics.
Degree: PhD, Electrical Engineering, 2007, Vanderbilt University
Robot-assisted rehabilitation has been an active research area for the last few years to automate therapy for regaining mobility with arm and hand movements following a deficiency in facility due to stroke. However, task-oriented therapy approaches that require patients to practice complex and more-functional activities of daily living (ADL) tasks cannot be performed by the existing robot-assisted rehabilitation systems because they are only limited to providing assistance to either arm or hand movement. Therefore, an intelligent controller for robot-assisted rehabilitation systems is desirable in order to perform ADL tasks that generally require coordination of both arm and hand movements.
In this dissertation, an intelligent control architecture is designed to coordinate in-house designed assistive devices in a systematic manner to enable the stroke patients to perform ADL tasks. The proposed control architecture is the first of its kind that brings the benefit of coordination of arm and hand assistive devices which is expected to address the deficit of coordinated assistive devices in the field of rehabilitation robotics. The control architecture is represented in terms of a hybrid system model combining a high-level controller for decision-making and two low-level assistive controllers (arm and hand controllers) for providing arm and hand motion assistance. The application of a hybrid system model for rehabilitation purposes is unique. Furthermore, providing robotic assistance to the patients to complete the rehabilitation task in a smooth manner is an important objective in rehabilitation therapies. Thus, the low-level assistive controllers in the control architecture are designed in such a way as to enhance smooth human-robot interaction involving the subject and the robotic assistive devices. Results from real-time assistance experiments on unimpaired subjects are presented to demonstrate the efficacy of the presented control architecture.
Advisors/Committee Members: Dr. Mitch Wilkes (committee member), Dr. George E. Cook (committee member), Dr. Michael Goldfarb (committee member), Dr. Thomas E. Groomes (committee member), Dr. Nilanjan Sarkar (Committee Chair).
Subjects/Keywords: Robots – Control systems; Cerebrovascular disease Patients Rehabilitation; robot-assisted rehabilitation for ADL tasks; gain scheduling; smooth interaction; human arm parameter estimation; robot-assisted rehabilitation; coordination of arm and hand assistive devices; hybrid system model; Computerized self-help devices for people with disabilities
to Zotero / EndNote / Reference
APA (6th Edition):
Erol, D. (2007). Design of an Intelligent Control Architecture for Rehabilitation Robotics. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12613
Chicago Manual of Style (16th Edition):
Erol, Duygun. “Design of an Intelligent Control Architecture for Rehabilitation Robotics.” 2007. Doctoral Dissertation, Vanderbilt University. Accessed December 02, 2020.
MLA Handbook (7th Edition):
Erol, Duygun. “Design of an Intelligent Control Architecture for Rehabilitation Robotics.” 2007. Web. 02 Dec 2020.
Erol D. Design of an Intelligent Control Architecture for Rehabilitation Robotics. [Internet] [Doctoral dissertation]. Vanderbilt University; 2007. [cited 2020 Dec 02].
Available from: http://hdl.handle.net/1803/12613.
Council of Science Editors:
Erol D. Design of an Intelligent Control Architecture for Rehabilitation Robotics. [Doctoral Dissertation]. Vanderbilt University; 2007. Available from: http://hdl.handle.net/1803/12613