2019 |
Hasan, C Z C; Jailani, R; Tahir, N M 2018-October , Institute of Electrical and Electronics Engineers Inc., 2019, ISSN: 21593442, (cited By 0). Abstract | Links | BibTeX | Tags: 10 Fold Cross Validation, 3D Modeling, Autism Spectrum Disorders, Biophysics, Clinical Decision Making, Computer Aided Diagnosis, Decision Making, Discriminant Analysis, Diseases, Gait Analysis, Gait Classification, Ground Reaction Forces, Neural Networks, Parameterization Techniques, Pattern Recognition, Petroleum Reservoir Evaluation, Program Diagnostics, Support Vector Machines, Targeted Treatment, Three-Dimensional @conference{Hasan20192436, title = {ANN and SVM Classifiers in Identifying Autism Spectrum Disorder Gait Based on Three-Dimensional Ground Reaction Forces}, author = {C Z C Hasan and R Jailani and N M Tahir}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063202256&doi=10.1109%2fTENCON.2018.8650468&partnerID=40&md5=c697d0c43ebd77d76d74cb3726872f42}, doi = {10.1109/TENCON.2018.8650468}, issn = {21593442}, year = {2019}, date = {2019-01-01}, journal = {IEEE Region 10 Annual International Conference, Proceedings/TENCON}, volume = {2018-October}, pages = {2436-2440}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {Autism spectrum disorder (ASD) is a complex and lifelong neurodevelopmental condition that occurs in early childhood and is associated with unusual movement and gait disturbances. An automated and accurate recognition of ASD gait provides assistance in diagnosis and clinical decision-making as well as improving targeted treatment. This paper explores the use of two well-known machine learning classifiers, artificial neural network (ANN) and support vector machine (SVM) in distinguishing ASD and normal gait patterns based on prominent gait features derived from three-dimensional (3D) ground reaction forces (GRFs) data. The 3D GRFs data of 30 children with ASD and 30 typically developing children were obtained using two force plates during self-determined speed of barefoot walking. Time-series parameterization techniques were applied to the 3D GRFs waveforms to extract the significant gait features. The stepwise method of discriminant analysis (SWDA) was employed to determine the dominant GRF gait features in order to classify ASD and typically developing groups. The 10-fold cross-validation test results indicate that the ANN model with three dominant GRF input features outperformed the kernel-based SVM models with 93.3% accuracy, 96.7% sensitivity, and 90.0% specificity. The findings of this study demonstrate the reliability of using the 3D GRF input features, in combination with SWDA feature selection and ANN classification model as an appropriate method that may be beneficial for the diagnosis of ASD gait as well as for evaluation purpose of the treatment programs. © 2018 IEEE.}, note = {cited By 0}, keywords = {10 Fold Cross Validation, 3D Modeling, Autism Spectrum Disorders, Biophysics, Clinical Decision Making, Computer Aided Diagnosis, Decision Making, Discriminant Analysis, Diseases, Gait Analysis, Gait Classification, Ground Reaction Forces, Neural Networks, Parameterization Techniques, Pattern Recognition, Petroleum Reservoir Evaluation, Program Diagnostics, Support Vector Machines, Targeted Treatment, Three-Dimensional}, pubstate = {published}, tppubtype = {conference} } Autism spectrum disorder (ASD) is a complex and lifelong neurodevelopmental condition that occurs in early childhood and is associated with unusual movement and gait disturbances. An automated and accurate recognition of ASD gait provides assistance in diagnosis and clinical decision-making as well as improving targeted treatment. This paper explores the use of two well-known machine learning classifiers, artificial neural network (ANN) and support vector machine (SVM) in distinguishing ASD and normal gait patterns based on prominent gait features derived from three-dimensional (3D) ground reaction forces (GRFs) data. The 3D GRFs data of 30 children with ASD and 30 typically developing children were obtained using two force plates during self-determined speed of barefoot walking. Time-series parameterization techniques were applied to the 3D GRFs waveforms to extract the significant gait features. The stepwise method of discriminant analysis (SWDA) was employed to determine the dominant GRF gait features in order to classify ASD and typically developing groups. The 10-fold cross-validation test results indicate that the ANN model with three dominant GRF input features outperformed the kernel-based SVM models with 93.3% accuracy, 96.7% sensitivity, and 90.0% specificity. The findings of this study demonstrate the reliability of using the 3D GRF input features, in combination with SWDA feature selection and ANN classification model as an appropriate method that may be beneficial for the diagnosis of ASD gait as well as for evaluation purpose of the treatment programs. © 2018 IEEE. |
2017 |
Ilias, S; Tahir, N M; Jailani, R Institute of Electrical and Electronics Engineers Inc., 2017, ISBN: 9781509009251, (cited By 0). Abstract | Links | BibTeX | Tags: Classification (of information), Discriminant Analysis, Diseases, Extraction, Feature Extraction, Gait Analysis, Gait Classification, Image Retrieval, Industrial Electronics, Kernel Function, Kinematic Parameters, Kinematics, Learning, Linear Discriminant Analysis, Machine Learning Approaches, Motion Analysis System, Polynomial Functions, Principal Component Analysis, Support Vector Machines, SVM Classifiers @conference{Ilias2017275, title = {Feature extraction of autism gait data using principal component analysis and linear discriminant analysis}, author = {S Ilias and N M Tahir and R Jailani}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034081031&doi=10.1109%2fIEACON.2016.8067391&partnerID=40&md5=7deaef6538413df7bfaf7cf723001d72}, doi = {10.1109/IEACON.2016.8067391}, isbn = {9781509009251}, year = {2017}, date = {2017-01-01}, journal = {IEACon 2016 - 2016 IEEE Industrial Electronics and Applications Conference}, pages = {275-279}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {In this research, the application of machine learning approach specifically support vector machine along with principal component analysis and linear discriminant analysis as feature extractions are evaluated and validated in discriminating gait features between normal subjects and autism children. Gait features of 32 normal and 12 autism children were recorded and analyzed using VICON motion analysis system and a force platform during normal walking. Here, twenty one gait features describing the three types of gait characteristics namely basic, kinetic and kinematic in these children are extracted. Further, with these gait features as input during classification, the ability of SVM as classifier are investigated using three different kernel functions specifically linear, polynomial, and radial basis. Results showed that LDA as feature extraction is the highest accuracy with kinematic parameters as gait features along with polynomial function as kernel for the SVM classifier. This finding proven that LDA is suitable as feature extraction and SVM is indeed apt as gait classifier in classifying the gait pattern autism and normal children. © 2016 IEEE.}, note = {cited By 0}, keywords = {Classification (of information), Discriminant Analysis, Diseases, Extraction, Feature Extraction, Gait Analysis, Gait Classification, Image Retrieval, Industrial Electronics, Kernel Function, Kinematic Parameters, Kinematics, Learning, Linear Discriminant Analysis, Machine Learning Approaches, Motion Analysis System, Polynomial Functions, Principal Component Analysis, Support Vector Machines, SVM Classifiers}, pubstate = {published}, tppubtype = {conference} } In this research, the application of machine learning approach specifically support vector machine along with principal component analysis and linear discriminant analysis as feature extractions are evaluated and validated in discriminating gait features between normal subjects and autism children. Gait features of 32 normal and 12 autism children were recorded and analyzed using VICON motion analysis system and a force platform during normal walking. Here, twenty one gait features describing the three types of gait characteristics namely basic, kinetic and kinematic in these children are extracted. Further, with these gait features as input during classification, the ability of SVM as classifier are investigated using three different kernel functions specifically linear, polynomial, and radial basis. Results showed that LDA as feature extraction is the highest accuracy with kinematic parameters as gait features along with polynomial function as kernel for the SVM classifier. This finding proven that LDA is suitable as feature extraction and SVM is indeed apt as gait classifier in classifying the gait pattern autism and normal children. © 2016 IEEE. |
Ilias, S; Tahir, N M; Jailani, R; Hasan, C Z C Linear Discriminant Analysis in Classifying Walking Gait of Autistic Children Conference Institute of Electrical and Electronics Engineers Inc., 2017, ISBN: 9781538614099, (cited By 0). Abstract | Links | BibTeX | Tags: Autism, Autistic Children, Children with Autism, Discriminant Analysis, Diseases, Extraction, Feature Extraction, Gait Analysis, Gait Classification, Kinematics, Linear Discriminant Analysis, Motion Analysis System, Neural Networks, Principal Component Analysis, Three-Dimensional @conference{Ilias201767, title = {Linear Discriminant Analysis in Classifying Walking Gait of Autistic Children}, author = {S Ilias and N M Tahir and R Jailani and C Z C Hasan}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048377850&doi=10.1109%2fEMS.2017.22&partnerID=40&md5=06de53be2b4f3976ddcc420067ab6e44}, doi = {10.1109/EMS.2017.22}, isbn = {9781538614099}, year = {2017}, date = {2017-01-01}, journal = {Proceedings - UKSim-AMSS 11th European Modelling Symposium on Computer Modelling and Simulation, EMS 2017}, pages = {67-72}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {The aim of this research is to investigate the effectiveness between Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) along with neural network (NN) in classifying the gait of autistic children as compared to control group. Twelve autistic children and thirty two normal children participated in this study. Firstly the walking gait of these two groups are acquired using VICON Motion Analysis System to extract the three dimensional (3D) gait features that comprised of 21 gait features namely five features from basic temporal spatial, five features represented the kinetic parameters and twelve features from kinematic. Further, PCA and LDA are utilized as feature extraction in determining the significant features among these gait features. With NN as classifier, results showed that LDA as feature extraction outperform PCA for classification of autism versus normal children namely kinematic gait patterns attained 98.44% accuracy followed by basic temporal spatial gait features with accuracy of 87.5%. © 2017 IEEE.}, note = {cited By 0}, keywords = {Autism, Autistic Children, Children with Autism, Discriminant Analysis, Diseases, Extraction, Feature Extraction, Gait Analysis, Gait Classification, Kinematics, Linear Discriminant Analysis, Motion Analysis System, Neural Networks, Principal Component Analysis, Three-Dimensional}, pubstate = {published}, tppubtype = {conference} } The aim of this research is to investigate the effectiveness between Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) along with neural network (NN) in classifying the gait of autistic children as compared to control group. Twelve autistic children and thirty two normal children participated in this study. Firstly the walking gait of these two groups are acquired using VICON Motion Analysis System to extract the three dimensional (3D) gait features that comprised of 21 gait features namely five features from basic temporal spatial, five features represented the kinetic parameters and twelve features from kinematic. Further, PCA and LDA are utilized as feature extraction in determining the significant features among these gait features. With NN as classifier, results showed that LDA as feature extraction outperform PCA for classification of autism versus normal children namely kinematic gait patterns attained 98.44% accuracy followed by basic temporal spatial gait features with accuracy of 87.5%. © 2017 IEEE. |
Hasan, C Z C; Jailani, R; Tahir, Md N Use of statistical approaches and artificial neural networks to identify gait deviations in children with autism spectrum disorder Journal Article International Journal of Biology and Biomedical Engineering, 11 , pp. 74-79, 2017, ISSN: 19984510, (cited By 1). Abstract | Links | BibTeX | Tags: Article, Artificial Neural Network, Autism, Body Height, Body Mass, Children, Clinical Article, Controlled Study, Discriminant Analysis, Early Diagnosis, Female, Gait, Gait Analysis, Gait Disorder, Human, Learning, Male, Pediatrics, School Child, Statistical Analysis, Statistics, Time Series Analysis @article{Hasan201774, title = {Use of statistical approaches and artificial neural networks to identify gait deviations in children with autism spectrum disorder}, author = {C Z C Hasan and R Jailani and N Md Tahir}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043500605&partnerID=40&md5=6f2ffe7c2f5daf9fd02d4456acb94438}, issn = {19984510}, year = {2017}, date = {2017-01-01}, journal = {International Journal of Biology and Biomedical Engineering}, volume = {11}, pages = {74-79}, publisher = {North Atlantic University Union NAUN}, abstract = {Automated differentiation of ASD gait from normal gait patterns is important for early diagnosis as well as ensuring rapid quantitative clinical decision and appropriate treatment planning. This study explores the use of statistical feature selection approaches and artificial neural networks (ANN) for automated identification of gait deviations in children with ASD, on the basis of dominant gait features derived from the three-dimensional (3D) joint kinematic data. The gait data from 30 ASD children and 30 normal healthy children were measured using a state-of-the-art 3D motion analysis system during self-selected speed barefoot walking. Kinematic gait features from the sagittal, frontal and transverse joint angles waveforms at the pelvis, hip, knee, and ankle were extracted using time-series parameterization. Two statistical feature selection techniques, namely the between-group tests (independent samples t-test and Mann-Whitney U test) and the stepwise discriminant analysis (SWDA) were adopted as feature selector to select the meaningful gait features that were then used to train the ANN. The 10-fold cross-validation test results indicate that the selected gait features using SWDA technique are more reliable for ASD gait classification with 91.7% accuracy, 93.3% sensitivity, and 90.0% specificity. The findings of the current study demonstrate that kinematic gait features with the combination of SWDA feature selector and ANN classifier would serve as a potential tool for early diagnosis of gait deviations in children with ASD as well as provide support to clinicians and therapists for making objective, accurate, and rapid clinical decisions that lead to the appropriate targeted treatments. © 2017 North Atlantic University Union NAUN. All Rights Reserved.}, note = {cited By 1}, keywords = {Article, Artificial Neural Network, Autism, Body Height, Body Mass, Children, Clinical Article, Controlled Study, Discriminant Analysis, Early Diagnosis, Female, Gait, Gait Analysis, Gait Disorder, Human, Learning, Male, Pediatrics, School Child, Statistical Analysis, Statistics, Time Series Analysis}, pubstate = {published}, tppubtype = {article} } Automated differentiation of ASD gait from normal gait patterns is important for early diagnosis as well as ensuring rapid quantitative clinical decision and appropriate treatment planning. This study explores the use of statistical feature selection approaches and artificial neural networks (ANN) for automated identification of gait deviations in children with ASD, on the basis of dominant gait features derived from the three-dimensional (3D) joint kinematic data. The gait data from 30 ASD children and 30 normal healthy children were measured using a state-of-the-art 3D motion analysis system during self-selected speed barefoot walking. Kinematic gait features from the sagittal, frontal and transverse joint angles waveforms at the pelvis, hip, knee, and ankle were extracted using time-series parameterization. Two statistical feature selection techniques, namely the between-group tests (independent samples t-test and Mann-Whitney U test) and the stepwise discriminant analysis (SWDA) were adopted as feature selector to select the meaningful gait features that were then used to train the ANN. The 10-fold cross-validation test results indicate that the selected gait features using SWDA technique are more reliable for ASD gait classification with 91.7% accuracy, 93.3% sensitivity, and 90.0% specificity. The findings of the current study demonstrate that kinematic gait features with the combination of SWDA feature selector and ANN classifier would serve as a potential tool for early diagnosis of gait deviations in children with ASD as well as provide support to clinicians and therapists for making objective, accurate, and rapid clinical decisions that lead to the appropriate targeted treatments. © 2017 North Atlantic University Union NAUN. All Rights Reserved. |
Testingadminnaacuitm2020-05-28T06:49:14+00:00
2019 |
2018-October , Institute of Electrical and Electronics Engineers Inc., 2019, ISSN: 21593442, (cited By 0). |
2017 |
Institute of Electrical and Electronics Engineers Inc., 2017, ISBN: 9781509009251, (cited By 0). |
Linear Discriminant Analysis in Classifying Walking Gait of Autistic Children Conference Institute of Electrical and Electronics Engineers Inc., 2017, ISBN: 9781538614099, (cited By 0). |
Use of statistical approaches and artificial neural networks to identify gait deviations in children with autism spectrum disorder Journal Article International Journal of Biology and Biomedical Engineering, 11 , pp. 74-79, 2017, ISSN: 19984510, (cited By 1). |