Integrating Multi-Sensors for Observing Post ACL Reconstruction Recovery Progress

This study investigates the integration of wireless micro-electro-mechanical-systems (MEMS) and electromyography (EMG) sensors for developing a motion analysis system for post-operative recovery monitoring of Anterior Cruciate Ligament (ACL) reconstructed subjects based on biofeedback mechanism. The kinematics and neuromuscular signals have been combined using mixed signal processing techniques and a feature set has been generated for classification of recovery status of subjects. Two intelligent techniques (Feed-forward Artificial Neural Network and Fuzzy Rule-based Classifier) have been tested and compared for providing rehabilitation status of the subjects. The system has been tested on a group of national athletes and it provides an un-obstructive assessment of the kinematics and neuromuscular changes occurring after ACL reconstruction in an athlete. The successful implementation and testing of multimodal sensors' integration show its feasibility in identifying the clinical stage of the recovery process of athletes after ACL repair and using it as an assistive tool for clinical decision-making during the rehabilitation regimen.

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