ASD Children Gait Classification Based On Principal Component Analysis and Linear Discriminant Analysis

Zakaria, Nur Khalidah

doi:10.30534/ijeter/2020/38862020

Cited by 9 publications

(9 citation statements)

References 30 publications

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“…For example, Vilensky et al [19] used two high-speed cameras, with one camera was placed perpendicularly with the walking track while the other camera was facing the track to measure gait disturbance between the normal and autistic children at the hip, knee, and ankle joints. Meanwhile, Zakaria et al [20] performed ASD gait classification based on the calculated distance between joints captured by the depth camera.…”

Section: Related Workmentioning

confidence: 99%

A Markerless-based Gait Analysis and Visualization Approach for ASD Children

Zakaria¹,

Tahir²,

Jailani³

2021

IJACSA

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This study proposed a new method in gait acquisition and analysis for autistic children based on the markerless technique versus the gold standard marker-based technique. Here, the gait acquisition stage is conducted using a depth camera with a customizable skeleton tracking function that is the Microsoft Kinect sensor for recording the walking gait trials of the 23 children with autism spectrum disorder (ASD) and 30 typically healthy developing (TD) children. Next, the Kinect depth sensor outputs information is translated into kinematic gait features. Further, analysis and evaluation are done specifically the kinematic angles of the hip, knee, and ankle in analyzing and visualizing the pattern of the plots versus the kinematic plots acquired from the marker-based that is the Vicon motion system gait technique. In addition, these kinematic angles are also validated using the statistical method namely the Analysis of Variance (ANOVA). Results showed that the ρ-values are insignificant for all angles upon computing both the intragroup and inter-group normalization. Hence, these findings have proven that the proposed markerless-based gait technique is indeed apt to be used as a new alternative markerless method for gait analysis of ASD children.

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Section: Related Workmentioning

confidence: 99%

A Markerless-based Gait Analysis and Visualization Approach for ASD Children

Zakaria¹,

Tahir²,

Jailani³

2021

IJACSA

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“…To further understand gait patterns in children with ASD versus neurotypical controls, there has been a recent increase in the number of studies using machine learning (ML) tools for classification based on combinations of kinematic and kinetic features [ 7 , 14 , 15 , 16 , 17 , 18 , 19 ]. Such studies facilitate the identification of discriminatory gait features in ASD versus neurotypical controls, which may lead to the development of automated gait classifiers, optimal treatment plans, improved functionality for children, and reduced health care costs.…”

Section: Introductionmentioning

confidence: 99%

“…Previous research has investigated the classification of ASD and control gait patterns using temporal-spatial, kinematic, and kinetic features [ 7 , 14 , 15 , 16 , 17 , 18 , 19 ]. Ilias et al, (2006) observed that the fusion of all these features resulted in a classification accuracy of 95.80% (sensitivity 100%, specificity 85.00%) using a support vector machine (SVM) classifier [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, a step-wise discriminant analysis feature selection was utilized for dimensionality reduction and demonstrated that the best performance could be attributed to four features, three of which were extracted from the ankle angle. Other dimensionality reduction techniques employed data transformations, such as principal component analysis and linear discriminant analysis [ 15 ] on three-dimensional joint position data, which resulted in an accuracy of 99.3% (sensitivity 99.66%, specificity 99.00%). More recently, a study extracted kinematic features from a markerless-based data acquisition [ 16 ] that resulted in a classification accuracy of 92% using a rough set classifier [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Autism and Control Gait in Children Using Multisegment Foot Kinematic Features

2022

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Previous research has demonstrated that children with autism walk with atypical ankle kinematics and kinetics. Although these studies have utilized single-segment foot (SSF) data, multisegment foot (MSF) kinematics can provide further information on foot mechanics. Machine learning (ML) tools allow the combination of MSF kinematic features for classifying autism gait patterns. In this study, multiple ML models are investigated, and the most contributing features are determined. This study involved 19 children with autism and 21 age-matched controls performing walking trials. A 34-marker system and a 12-camera motion capture system were used to compute SSF and MSF angles during walking. Features extracted from these foot angles and their combinations were used to develop support vector machine (SVM) models. Additional techniques-S Hapley Additive exPlanations (SHAP) and the Shapley Additive Global importancE (SAGE) are used for local and global importance of the black-box ML models. The results suggest that models based on combinations of MSF kinematic features classify autism patterns with an accuracy of 96.3%, which is higher than using SSF kinematic features (83.8%). The relative angle between the metatarsal and midfoot segments had the highest contribution to the classification of autism gait patterns. The study demonstrated that kinematic features from MSF angles, supported by ML models, can provide an accurate and interpretable classification of autism and control patterns in children.

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“…Several statistical studies were carried out to prove the sensitivity of features in measuring the expected pathologies. Zakaria et al [7] classified Autism Spectrum Disorder (ASD) children's gait from normal gait. Gait features were the absolute or relative Cartesian coordinates of 20 joints of the subject measured by a depth camera.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

Chen

Wang

et al. 2021

Preprint

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Background: Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information.Methods: In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of seventeen children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe in, toe out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified 10-fold cross-validation with recall, precision, and a time cost as metrics.Results: The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% recognition accuracy respectively in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing.Conclusions: In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

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ASD Children Gait Classification Based On Principal Component Analysis and Linear Discriminant Analysis

Cited by 9 publications

References 30 publications

A Markerless-based Gait Analysis and Visualization Approach for ASD Children

A Markerless-based Gait Analysis and Visualization Approach for ASD Children

Classification of Autism and Control Gait in Children Using Multisegment Foot Kinematic Features

Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

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