Research on Non-contact Monitoring Method of Human Knee Joint Movement State

Abstract

As people's awareness of sports health continues to increase, the risk of related sports injuries is also increasing. Nearly 70% of running depth enthusiasts have experienced or are suffering from running injuries, and the incidence of knee pain is about 30%. Therefore, the detection of knee joint health is of great significance. However, the traditional wearable sensor monitoring the health of the human knee joint has disadvantages such as the need for medical equipment assistance and difficulty in operation by non-medical personnel. In response to this situation, this article designed a non-contact real-time monitoring of the health of the human knee joint based on deep learning. system. The results show that the non-contact system can monitor the state of motion that is highly in line with the real situation. Comparing the evaluation effect of the traditional medical knee joint scoring scale, the non-contact system evaluation result achieved a performance with an error of 5.95%, which finally verified the practical value of the non-contact human knee joint health monitoring system.