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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...
In this study, we deemed further to evaluate the performance of Neural Network (NN) and Support Vector Machine (SVM) in classifying the gait patterns between autism and normal children. Firstly, temporal spatial, kinetic and kinematic gait parameters of forty four subjects namely thirty two normal subjects and twelve autism children are acquired. Next, these three category gait parameters acted as...
In this study, the effectiveness of Linear Discriminant Analysis (LDA) as feature extraction and dimensionality reduction is evaluated and compared with Singular Value Decomposition (SVD) for gait recognition of Parkinson Disease subjects as compared to normal subjects. Here, three feature vectors of gait namely basic, kinetic and kinematic features are extracted and analysed using LDA and leave one...
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