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Minimal information is known about the three-dimensional (3D) ground reaction forces (GRF) on the gait patterns of individuals with autism spectrum disorders (ASD). The purpose of this study was to investigate whether the 3D GRF components differ significantly between children with ASD and the peer controls. 15 children with ASD and 25 typically developing (TD) children had participated in the study...
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...
Recently, gait patterns of children with autism is of interest in the gait community in order to identify significant gait parameter namely the three dimensional (3D) gait features such as spatial temporal, kinematic and kinetic. This is because gait pattern provides clinicians and researchers in understanding the trajectory of gait development. Understanding the characteristics and identifying gait...
Cardiovascular disease is the main cause of human casualty in the world as reported by World Health Organization (WHO). To facilitate in minimizing this cause, constant monitoring of the heart's rhythm appears to be essential in observing the condition of the heart by checking or recording the Electrocardiogram (ECG) signal. This paper presents a prototype design of Single Wireless Electrocardiogram...
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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