The objective of this study is to propose an integrated motion analysis system for monitoring and assisting the rehabilitation process for athletes based on biofeedback mechanism, particularly for human subjects already undergone Anterior Cruciate Ligament (ACL) injury operations and thus about to start the rehabilitation process. For this purpose, different types of parameters (kinematics and neuromuscular signals) from multi-sensors integration are combined to analyze the motion of affected athletes. Signals acquired from sensors are pre-processed in order to prepare the pattern set for intelligent algorithms to be integrated for possible implementation of effective assistive rehabilitation processing tools for athletes and sports orthopedic surgeons. Based on the characteristics of different signals invoked during the rehabilitation process, two different intelligent approaches (Elman RNN and Fuzzy Logic) have been tested. The newly introduced integrated multi-sensors approach will assist in identifying the clinical stage of the recovery process of athletes after ACL repair and will facilitate clinical decision-making during the rehabilitation process. The use of wearable wireless miniature sensors will provide an un-obstructive assessment of the kinematics and neuromuscular changes occurring after ACL reconstruction in an athlete.

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