Use of Wearable Sensors and Machine Learning Methods in Promoting Total Joint Replacement Treatment Outcomes: A Survey

Abstract

Total Joint Replacement (TJR) surgeries are one of the most prevalent operations that are undergone by the elderly population. With the world population aging, the number of surgeries will continue to increase. A small portion of these surgeries result in complications that require readmissions. These readmissions amount to a significant financial and time burden for both the patients and the hospitals. In the past decade machine learning and wearable sensors have both been used extensively in the healthcare domain but the contribution to the prediction/evaluation and management of TJR is limited. What’s more, to our best knowledge there has been no effort in summarizing the findings from these studies. Therefore, this study highlights what has been achieved by using machine learning and wearable sensors in the TJR context and point out possible research avenues.