A knowledge-based intelligent framework for anterior cruciate ligament rehabilitation monitoring

Abstract

This study presents an integration of knowledge-based system and intelligent methods to develop a recovery monitoring framework for post anterior cruciate ligament (ACL) injured/reconstructed subjects. The case based reasoning methodology has been combined with fuzzy clustering and intelligent classification techniques in order to develop a knowledge base and a learning model for identifying the recovery stage of ACL-reconstructed subjects and objectively monitoring the progress during the convalescence regimen. The system records kinematics and neuromuscular signals from lower limbs of healthy and ACL-reconstructed subjects using self adjusted non-invasive body-mounted wireless sensors. These bio-signals are synchronized and integrated, and a combined feature set is generated by performing data transformation using wavelet decomposition and feature reduction techniques. The knowledge base stores the subjects’ profiles, their recovery sessions’ data and problem/solution pairs for different activities monitored during the course of rehabilitation. Fuzzy clustering technique has been employed to form the initial groups of subjects at similar stage of recovery. In order to classify the recovery stage of subjects (i.e. retrieval of similar cases), adaptive neuro-fuzzy inference system (ANFIS), fuzzy unordered rule induction algorithm (FURIA) and support vector machine (SVM) have been applied and compared. The system has been successfully tested on a group of healthy and post-operated athletes for analyzing their performance in two activities (ambulation at various speeds and one leg balance testing) selected from the rehabilitation protocol. The case adaptation and retention is a semi-automatic process requiring input from the physiotherapists and physiatrists. This intelligent framework 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/comparing athletes’ sports performance. Further, the knowledge base can easily be extended and enhanced for monitoring different types of sports activities.