Supporter-Type Anterior Cruciate Ligament Prevention System Based on Estimation of Knee Joint Valgus Angle Using Stretch Sensors

Abstract

Anterior cruciate ligament (ACL) injuries are common in sports involving jumping and rapid direction changes, often occurring in non-contact situations. The risk of ACL injury is evaluated by knee flexion and valgus angles; a small knee flexion angle combined with a large valgus angle increases the risk. Monitoring these angles during activities can help athletes recognize their ACL injury risk and adjust their movements. Traditional 3D motion analysis, used for measuring knee angles, is costly and impractical for daily practice. This study proposes a knee supporter with stretch sensors to estimate knee flexion and valgus angles in practice settings, evaluating ACL injury risk and notifying athletes of high-risk movements. The proposed device wirelessly transmits data from three stretch sensors placed on the device to a PC and uses machine learning to estimate the knee angles. The results of the evaluation experiments, conducted with data from five healthy male and female participants in their twenties, indicate that the estimation accuracy for the knee flexion angle, achieved by a model trained using a Random Forest Regressor (RFR) with data from individuals other than the target user, resulted in a Mean Absolute Error (MAE) of 8.86 degrees. For the knee valgus angle, a model trained with the user’s own data using the RFR achieved a MAE of 0.81 degrees.

Keywords

• knee valgus angle
• knee flexion angle
• wearable computing