Date of Award

August 2022

Degree Type

Thesis

Degree Name

Master of Science

Department

Engineering

First Advisor

Mohammad H Rahman

Committee Members

Ilya Avdeev, Inga Wang, Md Rasedul Islam

Keywords

Digital Twin, IIoT, Telerehabilitation, ThingWorx, Upper-limb rehabilitation, Vuforia Studio

Abstract

Upper limb dysfunction (ULD) is common following a stroke, spinal cord injury, trauma, and occupational accidents. Post-stroke patients with ULD need long-term assistance from therapists for their rehabilitation, which generally occurs at the hospital or outpatient clinic. Travel and transportation are significant factors that prevent patients from receiving adequate therapy, often leading to long-term disability. A home-based rehabilitation device providing essential arm movement therapies can significantly ease this rehabilitation program. In this research, we developed an end-effector type Desktop-Mounted Rehabilitation Robot (DMRbot) with a minimum viable design to cover the full range of human upper limb (UL) workspace to provide an essential UL rehab exercise. PTC's Industrial Internet of Things (IIoT) platform is used in this study to provide home-based rehabilitation therapies for individuals with ULD remotely. Remote rehabilitation is pragmatical because of the negligible latency (expedited through cloud services deployment by ThingWorx) and stable communication structure. With the developed telerehabilitation framework, an operator can teleoperate the DMRbot to deliver UL exercises via an Augmented Reality (AR) based graphical user interface (GUI), and a virtual joystick. This AR, platform communicates bidirectionally with the robot using ThingWorx IIOT. The developed telerehabilitation system also allows therapists to administer passive rehabilitation therapy using a physical joystick. This study leverages the digital twin structure, facilitated by Vuforia studio, to visualize the physical robot motions happening in remote places. Experiments were conducted to validate the novel telerehabilitation framework to provide remote therapy using the developed DMRbot. The result show that the DMRbot can be successfully controlled from the Vuforia studio AR platform and with a joystick to provide UL rehab exercises in 2D and 3D planes.

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