An Intelligent Recovery Progress Evaluation System for ACL Reconstructed Subjects Using Integrated 3-D Kinematics and EMG Features

Abstract

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.

Deep Autoencoder for Identification of Abnormal Gait Patterns Based on Multimodal Biosignals

“…In contrast to optical/camera systems, wearable wireless MEMS have also gained a lot of attention for human activity analysis due to their small size and easy setup in any kind of environment [3]. Multiple sensors' integration in wearable systems provide different signals at the same time which makes such a low-cost system very suitable for gait monitoring [5,9].…”

“…Further, since the lower limb injuries cause muscle atrophy and neuromuscular disorders so the electromyography signals have also been evaluated to monitor the changes in the gait patterns in few studies [14]. However, most of the existing studies focus on individual kinematics or sptiotemporal or electromyography signals and there exists only few studies which have integrated these biosignals to analyze the gait patterns in subjects [3,5,15].…”

“…The EMG signals captured through surface electrodes were wirelessly transmitted to the laptop using USB receiver and stored in CSV file format for further processing. Both kinematics and EMG systems were synchronized and data were segmented by detecting the heel strike event during the gait cycles [3,5].…”

“…Moreover, the raw EMG data with zero mean were band-pass filtered (20-450Hz) using 4th order Butterworth filter. More details about the hardware setup, synchronization of MEMS and EMG data, coordinate transformation and preparation of subjects were followed as mentioned in [3,5].…”

“…While for clinical applications, the focus of gait analysis is more on the recovery of the patients to the normal gait movements. Gait impairments are common in humans after lower limb or brain injuries [4,5]. These injuries result in kinematics and musculoskeletal changes which may persist and permanently alter the gait patterns if appropriate rehabilitation procedures are not followed.…”