Date of Award

2025

Degree Type

Thesis

Degree Name

Master of Science in Mechanical Engineering and Applied Mechanics

Department

Mechanical, Industrial and Systems Engineering

First Advisor

Musa Jouaneh

Abstract

Stroke is a leading cause of physical disability around the world, and the likelihood of stroke increases as people live longer. The current number of physiotherapists is insufficient to meet the increasing demand for their services. As a result, there has been a focus on developing robotic devices that function similarly to traditional therapy, enabling multiple patients to be seen simultaneously. While many devices have been created and tested, most are expensive, complex, and require trained personnel for supervision, thereby limiting their outreach. This thesis presents the design and control of a low-cost stroke therapy device designed to promote upper limb rehabilitation through four distinct operating modes. The device is a belt-driven, one-degree-of-freedom track and hand cart that aims to fill the gap for a likely low-cost at-home therapy device. Using four highly customizable and distinct operating modes, patients of all ability levels can receive personalized training plans in the comfort of their own home. The four modes consist of Passive, Assistive, Transparent, and Resistive, with each mode requiring more effort/ higher ability level than the previous. A custom control architecture was created to offer seamless transitions between modes and provide real-time feedback to the user. The custom architecture features a Graphical User Interface (GUI) that enables the patient to switch between operating modes, adjust device parameters, and display key performance metrics for easy viewing. Additionally, the GUI interfaces with the Performance Motion Devices (PMD Corp.) nIONCME developer kit for precise motion control. After constructing the device, the author conducted a series of trials to evaluate its performance. Additionally, multiple simulation models were developed to predict device behavior under various inputs, thereby facilitating easier device tuning and prediction for different ability levels. Results demonstrate the reliability and customization of device parameters, showing promise as a portable at-home therapy device.

Comments

Additional Files Descriptions:

AssistTrajectory.c - Assistive mode code

ComFunctions.c - Serial Communication Code

HomeCapture.c - Homing code

LoadCellFunctions.c - Load cell functions

nIONCME.c - Main loop

PassiveMode2.c - Passive mode code

ResistTorqueGoal2.c - Resistive mode code

ReturnHome.c - Code to return to home position

ReverseMove.c - Code for moving device towards right limit switch

Stroke GUI.py - Code for GUI

Transparent.c - Transparent mode code

AssistTrajectory.c (24 kB)
AssistTrajectory.c

ComFunctions.c (13 kB)
ComFunctions.c

HomeCapture.c (3 kB)
HomeCapture.c

LoadCellFunctions.c (4 kB)
LoadCellFunctions.c

nIONCME.c (29 kB)
nIONCME.c

PassiveMode2.c (15 kB)
PassiveMode2.c

ResistTorqueGoal2.c (9 kB)
ResistTorqueGoal2.c

ReturnHome.c (1 kB)
ReturnHome.c

ReverseMove.c (1 kB)
ReverseMove.c

Stroke GUI.py (72 kB)
Stroke GUI.py

Transparent.c (7 kB)
Transparent.c

Available for download on Monday, September 07, 2026

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