Date of Award
Spring 2025
Document Type
Restricted Thesis
Terms of Use
© 2025 Justin Gonzalez. All rights reserved. Access to this work is restricted to users within the Swarthmore College network and may only be used for non-commercial, educational, and research purposes. Sharing with users outside of the Swarthmore College network is expressly prohibited. For all other uses, including reproduction and distribution, please contact the copyright holder.
Degree Name
Bachelor of Arts
Department
Engineering Department
First Advisor
Joseph D. Towles
Abstract
Shoulder pain affects a large percentage of the US population, and while many seek physical therapy as rehabilitation, there are still problems that arise. Shoulder rehabilitation often suffers from muscle compensation and limited quantitative feedback, and additionally, a primary concern of a physical therapist is ensuring that the patients are performing the exercises correctly without the supervision of the clinician. Therefore, this project developed a dual-system electromyography (EMG) biofeedback platform to improve muscle awareness and tracking during shoulder exercises. The first system uses a Delsys Trigno EMG setup with a custom Python GUI for in-clinic use. The second system, built with Arduino Uno and MyoWare sensors, provides a low-cost, portable solution for at-home use. Both systems deliver real-time EMG signal visual feedback to patients, with the Arduino Unit achieving a sub-second latency and a total cost of under $200. This work offers a foundation for future clinical integration of EMG-based feedback systems for shoulder rehabilitation.
Recommended Citation
Gonzalez, Justin , '25, "Biofeedback System for Shoulder Rehabilitation" (2025). Senior Theses, Projects, and Awards. 963.
https://works.swarthmore.edu/theses/963
