Wearable System for Knee Rehabilitation Monitoring with Near Real-time Feedback

Abstract

Patients performing knee rehabilitation using physiotherapy outside clinical settings have no reliable means of verifying whether their movements meet therapeutic standards. This paper presents a pasteable, skin-mounted sensing system that combines lightweight inertial measurement units (IMUs) with machine learning to monitor rehabilitation exercises in everyday environments. Kinematic signals, including IMU orientation angles, angular velocity, and linear acceleration, are extracted from segmented motion cycles and used as statistical features for movement quality assessment. Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM) classifiers are evaluated for normal-versus-abnormal execution detection, with Random Forest achieving 98.22\% accuracy and 99.97\% AUC. When abnormal motion is detected, the system delivers near real-time haptic feedback at the sensor node, which allows immediate self-correction without clinician involvement, offering a usable solution for home and community-based rehabilitation.