S. M. N. Arosha Senanayake

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

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.

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.

Wireless Multi-Sensor Integration for ACL Rehabilitation Using Biofeedback Mechanism

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.Copyright

Neural computing for walking gait pattern identification based on multi-sensor data fusion of lower limb muscles

The use of neural computing for gait analysis widely known as computational intelligent gait analysis is addressed recently. This research work reports multilayer feed-forward neural networks for walking gait pattern identification using multi-sensor data fusion; electromyography (EMG) signals and soft tissue deformation analysis using successive frames of video sequence extracted from lower limb muscles according to each gait phase within the considered gait cycle. Neural computing framework for walking gait pattern identification consists of system hardware and intelligent system software. System hardware comprises a wireless surface EMG sensor unit and two video cameras for measuring the neuromuscular activity of lower limb muscles, and a custom-developed artificial neural network for classifying the gait patterns of subjects during walking. The system uses root mean square and soft tissue deformation parameter as the input features. Multilayer feed-forward back propagation neural networks (FFBPNNs) with different network training functions were designed, and their classification results were compared. The intelligent gait analysis system validation has been carried out for a group of healthy and injured subjects. The results demonstrated that the overall accuracy of 98xa0% prediction is achieved for gait patterns classification established by multi-sensor data fusion of lower limb muscles using FFBPNN with Levenberg–Marquardt training function resulting better performance over FFBPNN with other training functions.

A Multisensor Integration-Based Complementary Tool for Monitoring Recovery Progress of Anterior Cruciate Ligament-Reconstructed Subjects

Anterior cruciate ligament (ACL) trauma, being one of the most common musculoskeletal injuries in sports, leads to knee joint instability and causes ambulation impairments. A careful monitoring of the progress of recovery after ACL reconstruction is crucial for minimizing postoperative complications and reinjuries. This research is aimed at designing a complementary tool to assess the recovery status and knee dynamics during the rehabilitation period after ACL reconstruction. The prototype includes wireless body-mounted motion sensors for kinematics measurements, surface electromyography system for muscle activity measurements, a video camera for recording trial activities and custom-developed intelligent system software that provides classification of the progress of the recovery and visual biofeedback during rehabilitation. The subjects recovery stages are classified based on combined features from sensors data, using an adaptive neuro-fuzzy inference system. The visual biofeedback provides monitoring of different signals simultaneously in order to help in detecting the intra and intersubject variability and correlation between the knee joint dynamics and muscle activities. The promising results of this initial study for assessing the ambulation at various speeds showcase the prospects of using the proposed system as part of existing rehabilitation monitoring procedures to achieve a more effective and timely recovery of ACL-reconstructed subjects.

Artificial neural network based gait patterns identification using neuromuscular signals and soft tissue deformation analysis of lower limbs muscles

The objective of this study is to investigate the use of electromyography (EMG) signals and video based soft tissue deformation (STD) analysis for identifying the gait patterns of healthy and injured subjects. The system includes a wireless surface electromyography (EMG) sensor unit and two video camera systems for measuring the neuromuscular activity of lower limb muscles, and a custom-developed artificial neural network based intelligent system software for identifying the gait patterns of subjects during walking activity. The system uses root mean square (RMS) value of EMG signals and soft tissue deformation parameter (STDP) as the input features. In order to estimate the STD during a muscular contraction while walking, flexible triangular meshes are built on reference points. The positions of these selected points are evaluated by applying the block matching motion estimation technique. Based on the extracted features, multilayer feed-forward backpropagation networks (FFBPNNs) with different network training functions were designed and their classification performances were compared. The system has been tested for a group of healthy and injured subjects. The results showed that FFBPNN with Levenberg-Marquardt training function provided better prediction behavior (98% overall accuracy) as compared to FFBPNN with other training functions for gait patterns identification based on RMS value of EMG and STDP.

3-D kinematics and neuromuscular signals' integration for post ACL reconstruction recovery assessment

An intelligent recovery classification and monitoring system (IRCMS) for post Anterior Cruciate Ligament (ACL) reconstruction has been developed in this study. This system provides an objective assessment and monitoring of the rehabilitation progress by integrating 3-D kinematics and neuromuscular signals recorded through wearable motion and electromyography sensors, respectively. The data from a group of healthy and ACL reconstructed subjects were collected for normal/brisk walking (4-6km/h) and single leg balance (eyes open and eyes closed) testing activities. Fuzzy clustering and fuzzy nearest neighbor methods have been used to classify the collected data into different groups for each activity. The classification accuracy of the system is found to be 94.49% for 4 km/h walking speed, 95.41% for 5 km/h walking speed, 96.00% for 6 km/h walking speed, 94.44% for single leg balance testing with eyes open and 95.83% for single leg balance testing with eyes closed. The recovery status of a subject is evaluated based on different activities assessed and the overall assessment is done using Choquet integral fusion technique. Further, biofeedback mechanism has been developed using a visual monitoring system which provides the variations in strength/activation of knee flexors/extensors and 3-D joint kinematics. This integrated system can be used as an assistive tool by sports trainers, coaches and clinicians for monitoring overall progress of athletes rehabilitation and classifying their recovery stage for multiple activities.

An Interval Type-2 Fuzzy Logic Based Classification Model for Testing Single-Leg Balance Performance of Athletes after Knee Surgery

Single-leg balance test is one of the most common assessment methods in order to evaluate the athletes’ ability to perform certain sports actions efficiently, quickly and safely. The balance and postural control of an athlete is usually affected after a lower limb injury. This study proposes an interval type-2 fuzzy logic (FL) based automated classification model for single-leg balance assessment of subjects after knee surgery. The system uses the integrated kinematics and electromyography (EMG) data from the weight-bearing leg during the balance test in order to classify the performance of a subject. The data are recorded through wearable wireless motion and EMG sensors. The parameters for the membership functions of input and output features are determined using the data recorded from a group of athletes (healthy/having knee surgery) and the recommendations from physiotherapists and physiatrists, respectively. Four types of fuzzy logic systems namely type-1 non-singleton interval type-2 (NSFLS type-2), singleton type-2 (SFLS type-2), non-singleton type-1 (NSFLS type-1) and singleton type-1 (SFLS type-1) were designed and their performances were compared. The overall classification accuracy results show that the interval type-2 FL system outperforms the type-1 FL system in classifying the balance test performance of the subjects. This pilot study suggests that a fuzzy logic based automated model can be developed in order to facilitate the physiotherapists and physiatrists in determining the impairments in the balance control of the athletes after knee surgery.

A Real-Time Intelligent Biofeedback Gait Patterns Analysis System for Knee Injured Subjects

This study presents a real-time visualization system of gait patterns of knee injured subjects for biofeedback monitoring and classification. The developed system includes non-invasive wireless body-mounted motion sensors for kinematics measurements of lower extremities, surface electromyography (EMG) system for relevant specific muscle activity measurements, a motion capture system for recording trial activities and custom-developed intelligent system software implemented using LabVIEW and MATLAB. The real-time biofeedback system provides a visual monitoring of individual and superimposed signals (kinematics, EMG and video data) in order to identify the knee joint abnormality and muscles strength during various ambulation activities performed by the subjects. It can facilitate the clinicians, physiotherapists and physiatrists in determining the impairments in the gait patterns the knee injured based on the data collected and identifying the subjects lacking behind the desired level of recuperation.

An Integrated Pattern Recognition System for Knee Flexion Analysis

The purpose of this study is to propose an integrated knee-flexion analysis system (IKAS) as a novel tool for recognition pattern of knee muscle for athletes and soldiers based on neuromuscular signals and soft tissue deformation parameter. Different types of parameters from multi-sensors integration are combined to analyze the knee motion. Data fusion of EMG and frames of the video for each knee flexion angle acquired from synchronization of the motion capture system and video cameras interfaced with wireless EMG sensors. Systems are pre-processed in order to prepare the pattern set for a custom-developed artificial neural network and mesh generation technique based intelligent system for classifying the patterns of knee muscle of subjects during walking and squatting activity. Multilayer feed-forward backpropagation networks (FFBPNNs) with different network training algorithm were designed and coefficient correlation (CC) was uses and their classification results were compared. The newly introduced IKAS approach will provides assistance in making an objective and knowledgeable decisions about recognition of patterns from knee mm knee muscles.