IMU Sensor Fusion Algorithm for Monitoring Knee Kinematics in ACL Reconstructed Patients

In this paper we propose a sensor embedded knee brace to monitor knee flexion and extension and other lower limb joint kinematics after anterior cruciate ligament (ACL) injury. The system can be easily attached to a standard post-surgical brace and uses a novel sensor fusion algorithm that does not require calibration. The wearable system and the sensor fusion algorithm were validated for various physical therapy exercises against a validated motion capture system. The proposed sensor fusion algorithm demonstrated significantly lower root-mean-square error (RMSE) than the benchmark Kalman filtering algorithm and excellent correlation coefficients (CCC and ICC). The demonstrated error for most exercises was lower than other devices in the literature. The quantitative measures obtained by this system can be used to obtain longitudinal range-of-motion and functional biomarkers. These biomarkers can be used to improve patient outcomes through the early detection of at-risk patients, tracking patient function outside of the clinic, and the identification of relationships between patient presentation, intervention, and outcomes.