Date of Award

6-6-2022

Document Type

Thesis - SCU Access Only

Publisher

Santa Clara : Santa Clara University, 2022.

Departments

Bioengineering; Computer Science and Engineering

First Advisor

Behnam Dezfouli

Second Advisor

Yuling Yan

Abstract

Over 250 million people are a↵ected by knee osteoarthritis worldwide, causing chronic joint pain and sti↵ness as well as a general deterioration in quality of life. With existing technologies, it is difficult to predict the evolution of one’s condition and there is often an imbalance between the patient’s pain and the radiographic images, making osteoarthritis difficult to understand and evaluate. We are proposing a wearable, knee sleeve device that uses accelerometer sensors to detect joint stress and friction through frequency readings to more accurately assess the progression of osteoarthritis. Using signal processing and machine learning classification algorithms, our device provides a numerical value correlating with the severity of the patient’s condition.

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