Advanced Joint Monitoring System Utilizing Real-time Data and KNN Algorithm for Prediction of Joint Health

This research proposes a novel strategy for the field of tele-orthopedics by incorporating a K-Nearest Neighbors (KNN) algorithm into a Raspberry Pi-based remote joint monitoring system. The KNN model can provide accurate predictions about the present condition of the joint health of orthopedic patients by using real-time data from sensors that capture joint movement measures. The technology makes it easier for medical experts to intervene at the appropriate moment, which in turn makes it possible to design treatment programs that are more individualized and data-driven. The solution enables patients and healthcare practitioners to communicate without interruptions by using a protected platform hosted in the cloud. The malleability and interpretability of the KNN algorithm contribute to a sophisticated framework for tele-orthopedics, which offers precise insights into the development of orthopedic therapies. This study lays a framework for future developments in remote patient monitoring for orthopedic treatment. It proves that it is possible to use machine learning in telemedicine, which is a significant contribution to the field.