Title:  Enabling remote monitoring of joint health with signal processing and machine learning

Committee: 

Dr. Inan, Advisor 

Dr. Anderson, Chair

Dr. Millard-Stafford

Dr. Medda

Abstract: The objective of the proposed work is to enable at-home joint health sensing with a smart wearable knee brace through the use of signal processing and machine learning. Current diagnostic and monitoring techniques for orthopedic injuries and different forms of arthritis require clinical exams as well as imaging or blood tests. As such, the evaluation of different treatments is time consuming, expensive, and can be subjective. Wearable sensing is one option to overcome these difficulties. Joint acoustic emissions (JAEs), electrical bioimpedance (EBI), and kinematics are three noninvasive sensing modalities that have been investigated for joint health sensing and have been integrated into a wearable device. The current state of JAE analysis relies on benchtop setups and controlled recordings. To transition to wearable sensing, accurate, generalizable, and consistent tools need to be developed. To accomplish this, a signal quality assessment algorithm for JAEs was developed and validated to overcome potential artifacts introduced when collecting data in an uncontrollable environment. A clinical study on rheumatoid arthritis (RA) was then completed using the sensing brace to quantify disease activity. The signal quality assessment algorithm was utilized to clean the JAE signals, and machine learning models were developed using JAEs, EBI, and kinematics to predict disease activity and inflammation levels. As part of the proposed work, a generalizable model for healthy, meniscus tear, and RA patients will be developed using JAEs. This model is intended to provide information on the structure and inflammatory conditions in the knee which will then be transferred to the osteoarthritis (OA) domain.