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Malik O.A.,University of Brunei Darussalam | Senanayake S.M.N.A.,University of Brunei Darussalam | Zaheer D.,Sports Medicine and Research Center
IEEE International Conference on Fuzzy Systems | Year: 2014

This paper aims to investigate a gait pattern classification system for anterior cruciate ligament reconstructed (ACL-R) subjects based on the interval type-2 fuzzy logic (FL). The proposed system intends to model the uncertainties present in kinematics and electromyography (EMG) data used for gait analysis due to intra- and inter-subject stride-to-stride variability and nature of signals. Four features were selected from kinematics and EMG data recorded through wearable wireless sensors. The parameters for the membership functions of these input features were determined using the data recorded for 12 healthy and ACL-R subjects. The parameters for output membership functions and rules were chosen based on the recommendations from physiotherapists and physiatrists. The system was trained by using steepest descent method and tested for singleton and non-singleton inputs. The overall classification accuracy results show that the interval type-2 FL system outperforms the type-1 FL system in recognizing the gait patterns of healthy and ACL-R subjects. © 2014 IEEE.


Senanayke S.M.N.A.,University of Brunei Darussalam | Malik O.A.,University of Brunei Darussalam | Iskandar Pg.M.,University of Brunei Darussalam | Zaheer D.,Sports Medicine and Research Center
International Conference on Intelligent Systems Design and Applications, ISDA | Year: 2012

This paper presents a hybrid intelligent system for recovery and performance evaluation of athletes after anterior cruciate ligament (ACL) injury/reconstruction. The fuzzy logic and case based reasoning approaches have been combined to build an assistive tool for sports trainers, coaches and clinicians for maintaining athletes' profile, monitoring progress of recovery, classifying recovery status and adjusting the recovery protocols for individuals. The kinematics and neuromuscular data are collected for subjects after ACL injury/reconstruction using self adjusted body-mounted wireless sensors Upon feature extraction and transformation using principal component analysis, the fuzzy clustering with automatic detection of clusters is employed to group the data according to current recovery status. A knowledge base has been designed to store subjects' profiles, recovery sessions' data and problem/solution pairs. The recovery classification and selection of similar cases has been done using fuzzy k-nearest neighbor (f-knn) and cosine similarity measure. Once relevant cases are selected, adaptation is performed and the performance evaluation will be done. The proposed system has been tested on a group of healthy and post-operated athletes and the classification accuracy of the system is found to be more than 94% using leave-one out cross validation method for walking/running activity. © 2012 IEEE.


Malik O.A.,University of Brunei Darussalam | Senanayake S.M.N.A.,University of Brunei Darussalam | Zaheer D.,Sports Medicine and Research Center
IEEE/ASME Transactions on Mechatronics | Year: 2015

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. © 1996-2012 IEEE.


Malik O.A.,University of Brunei Darussalam | Senanayake S.M.N.A.,University of Brunei Darussalam | Zaheer D.,Sports Medicine and Research Center
IEEE Journal of Biomedical and Health Informatics | Year: 2015

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. © 2013 IEEE.


Senanayake S.M.N.A.,University of Brunei Darussalam | Malik O.A.,University of Brunei Darussalam | Iskandar M.,University of Brunei Darussalam | Zaheer D.,Sports Medicine and Research Center
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2013

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. © 2013 IEEE.


Arosha Senanayake S.M.N.,University of Brunei Darussalam | Malik O.A.,University of Brunei Darussalam | Iskandar P.M.,University of Brunei Darussalam | Zaheer D.,Sports Medicine and Research Center
Applied Soft Computing Journal | Year: 2014

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. © 2013 Elsevier B.V.


Arosha Senanayake S.M.N.,University of Brunei Darussalam | Malik O.A.,University of Brunei Darussalam | Iskandar P.M.,University of Brunei Darussalam | Zaheer D.,Sports Medicine and Research Center
Journal of Medical Engineering and Technology | Year: 2013

A hardware/software co-design for assessing post-Anterior Cruciate Ligament (ACL) reconstruction ambulation is presented. The knee kinematics and neuromuscular data during walking (2-6kmh-1) have been acquired using wireless wearable motion and electromyography (EMG) sensors, respectively. These signals were integrated by superimposition and mixed signals processing techniques in order to provide visual analyses of bio-signals and identification of the recovery progress of subjects. Monitoring overlapped signals simultaneously helps in detecting variability and correlation of knee joint dynamics and muscles activities for an individual subject as well as for a group. The recovery stages of subjects have been identified based on combined features (knee flexion/extension and EMG signals) using an adaptive neuro-fuzzy inference system (ANFIS). The proposed system has been validated for 28 test subjects (healthy and ACL-reconstructed). Results of ANFIS showed that the ambulation data can be used to distinguish subjects at different levels of recuperation after ACL reconstruction. © 2013 Informa UK Ltd.

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