Date of Graduation
5-2020
Document Type
Thesis
Degree Name
Bachelor of Science in Computer Engineering
Degree Level
Undergraduate
Department
Computer Science and Computer Engineering
Advisor/Mentor
Nelson, Alexander
Committee Member/Reader
Parkerson, James
Committee Member/Second Reader
Andrews, David
Abstract
Effective monitoring of adherence to at-home exercise programs as prescribed by physiotherapy protocols is essential to promoting effective rehabilitation and therapeutic interventions. Currently physical therapists and other health professionals have no reliable means of tracking patients' progress in or adherence to a prescribed regimen. This project aims to develop a low-cost, privacy-conserving means of monitoring at-home exercise activity using a gym mat equipped with an array of capacitive sensors. The ability of the mat to classify different types of exercises was evaluated using several machine learning models trained on an existing dataset of physiotherapy exercises.
Citation
Goertz, A. (2020). A Capacitive Sensing Gym Mat for Exercise Classification & Tracking. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/75
Included in
Artificial Intelligence and Robotics Commons, Other Computer Engineering Commons, Physical Therapy Commons
