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. |
Testingadminnaacuitm2020-05-28T06:49:14+00:00
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). |