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An intelligent recovery evaluation system is presented for objective assessment and performance monitoring of anterior cruciate ligament reconstructed (ACL-R) subjects. The system acquires 3-D kinematics of tibiofemoral joint and electromyography (EMG) data from surrounding muscles during various ambulatory and balance testing activities through wireless body-mounted inertial and EMG sensors, respectively. An integrated feature set is generated based on different features extracted from data collected for each activity. The fuzzy clustering and adaptive neuro-fuzzy inference techniques are applied to these integrated feature sets in order to provide different recovery progress assessment indicators (e.g., current stage of recovery, percentage of recovery progress as compared to healthy group, etc.) for ACL-R subjects. The system was trained and tested on data collected from a group of healthy and ACL-R subjects. For recovery stage identification, the average testing accuracy of the system was found above 95% (95-99%) for ambulatory activities and above 80% (80-84%) for balance testing activities. The overall recovery evaluation performed by the proposed system was found consistent with the assessment made by the physiotherapists using standard subjective/objective scores. The validated system can potentially be used as a decision supporting tool by physiatrists, physiotherapists, and clinicians for quantitative rehabilitation analysis of ACL-R subjects in conjunction with the existing recovery monitoring systems.

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