In-clinic wearable sensors for total knee replacement recovery analysis

Total knee replacement is the only viable solution for end-stage knee osteoarthritis causing pain and impairment for millions of people. Commonality of the surgery is increasing with an aging population and is being performed on younger patients due to improvements in implant longevity but a high self-reported dissatisfaction rate of up to 20% persists. Dissatisfied patients require more recovery resources, straining an already burdened healthcare system, and preventing allocation of resources new patients. A strong predictor of satisfaction is preoperative function, which can also predict the amount of improvement experienced post-operation, however we do not currently have the tools to measure patient function efficiently in the clinic. This project outlines the development of a wearable sensor system designed to measure patient function and make predictions about functional recovery following surgery.

Faculty Supervisor:

Matthew Teeter

Student:

Riley Bloomfield

Partner:

I-INC Foundation for Business Development

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

Western University