Anterior cruciate ligament recovery monitoring system using hybrid computational intelligent techniques

Abstract

A recovery monitoring system, based on hybrid computational intelligent techniques, is presented for post anterior cruciate ligament (ACL) injured/reconstructed subjects. The case based reasoning methodology has been combined with fuzzy and neuro-fuzzy techniques in order to develop a knowledge base and a learning model for classification of recovery stages and monitoring the progress of ACL-reconstructed subjects during the convalescence regimen. The system records kinematics and neuromuscular parameters from lower limbs of healthy and ACL-reconstructed subjects using body-mounted wireless sensors and a combined feature set is generated by performing data transformation and feature reduction techniques. In order to classify the recovery stage of subjects, fuzzy k-nearest neighbor technique and adaptive neuro-fuzzy inference system have been applied and results have been compared. The system was successfully tested on a group of healthy and post-operated athletes for analyzing their performance during ambulation and single leg balance testing activities. A semi-automatic process has been employed for case adaptation and retention, requiring input from the physiotherapists and physiatrists. The system can be utilized by physiatrists, physiotherapists, sports trainers and clinicians for multiple purposes including maintaining athletes' profile, monitoring progress of recovery, classifying recovery status, adapting recovery protocols and predicting athletes' sports performance