Classification of autism children gait patterns using Neural Network and Support Vector Machine

Ilias, Suryani; Tahir, Nooritawati Md; Jailani, Rozita; Hasan, Che Zawiyah Che

doi:10.1109/iscaie.2016.7575036

Cited by 22 publications

(23 citation statements)

References 25 publications

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“…However, it got 83.4% accuracy with a linear support vector machine (SVM) technique. Ilias et al [46] used fusion of tempo-spatial and kinematic features to classify gait patterns, and this approach got 95% accuracy with neural network classifier and SVM with polynomial kernel individually. With SVM polynomial as kernel attains 100% sensitivity and 85% specificity, show the efficacy of their approach in utilizing the SVM to identify autism.…”

Section: Abnormal Gait Recognitionmentioning

confidence: 99%

Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

Shahid¹,

Rahman²,

Ahmed³

et al. 2020

Preprint

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Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social interactions, cognitive skills, and abilities. Since ASD can last during an affected person's entire life cycle, the diagnosis at the early onset can yield a significant positive impact. The current medical diagnostic systems (e.g., DSM-5/ICD-10) are somewhat subjective; rely purely on the behavioral observation of symptoms, and hence, some individuals often go misdiagnosed or late-diagnosed. Therefore, researchers have focused on developing data-driven automated diagnosis systems with less screening time, low cost, and improved accuracy while significantly reducing professional intervention. Human Activity Analysis (HAA) is considered one of the most promising niches in computer vision research. This paper aims to analyze its potentialities in the automated detection of autism by tracking the exclusive characteristics of autistic individuals such as repetitive behavior, atypical walking style, and unusual visual saliency. This review provides a detailed inspection of HAA-based autism detection literature published in 2011 on-wards depicting core approaches, challenges, probable solutions, available resources, and scopes of future exploration in this arena. According to our study, deep learning outperforms machine learning in ASD detection with a classification accuracy of 76\% to 95\% on different datasets comprise of video, image, or skeleton data that recorded participants performing a large number of actions. However, machine learning provides satisfactory results on datasets with a small number of action classes and has a range of 60\% to 93\% accuracy among numerous studies. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

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Section: Abnormal Gait Recognitionmentioning

confidence: 99%

Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

Shahid¹,

Rahman²,

Ahmed³

et al. 2020

Preprint

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“…In [17], spatial-temporal gait features were applied to RFs, SVMs and a Kernel Fisher Discriminant (KFD), with the purpose to perform classification to detect those with Parkinson's Disease from those without. In [18], force plates were used to gather spatial-temporal, kinetic and kinematic gait parameters before applying them to an SVM and an Artificial Neural Network (ANN) to perform automatic autism classification.…”

Section: Introductionmentioning

confidence: 99%

Effects of Intra-Subject Variation in Gait Analysis on ASD Classification Performance in Machine Learning Models

Henderson

Yogarajah

Gardiner

et al. 2020

2020 31st Irish Signals and Systems Conference (ISSC)

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Autism Spectrum Disorder (ASD) is a developmental disorder that is prevalent globally. Research into detecting autism traditionally focused on behavioural aspects of the condition, however, more recently, focus has shifted to more objective alternatives using techniques such as machine learning and gait analysis. Gait measurements, having been used for person identification, varies from person to person, introducing a lot of intra-subject variance. This applies to the 8 spatialtemporal features used in this study, representing the time that an individual spends in each phase of a gait cycle, collected using a Vicon motion tracking system. The features were averaged across each gait trial that the subjects performed, producing a second set of features with reduced intra-subject variance. Four common classifiers, a Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forests (RF) and a Decision Tree (DT) classifier, were all trained using the two feature sets and their classification rates were compared. The results show that for the RF classifier, reducing the intra-subject variance, was able to successfully increase the classification power. The KNN and DT classifiers experienced a minimal decrease in accuracy, where the SVM suffered the greatest loss when intrasubject variance was reduced. Results overall show that the effect intra-subject variance has on classification power depends heavily on the suitability of the classifier to the initial problem as well as size and class balance of the data.

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“…Since the deep-learning artificial neural network is a data-driven method, has the ability to find characteristics hidden in the complete data set. We hypothesize that deep-leaning model might be used for the prediction of ASD through brain images, in particular, our fNIRS data collected from children with ASD, though deep learning based approaches have not been well studied (Ilias et al, 2016; Dvornek et al, 2017; Chiarelli et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Prediction in Autism by Deep Learning Short-Time Spontaneous Hemodynamic Fluctuations

Geng

et al. 2019

Front. Neurosci.

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This study aims to explore the possibility of using a multilayer artificial neural network for the classification between children with autism spectrum disorder (ASD) and typically developing (TD) children based on short-time spontaneous hemodynamic fluctuations. Spontaneous hemodynamic fluctuations were collected by a functional near-infrared spectroscopy setup from bilateral inferior frontal gyrus and temporal cortex in 25 children with ASD and 22 TD children. To perform feature extraction and classification, a multilayer neural network called CGRNN was used which combined a convolution neural network (CNN) and a gate recurrent unit (GRU), since CGRNN has a strong ability in finding characteristic features and acquiring intrinsic relationship in time series. For the training and predicting, short-time (7 s) time-series raw functional near-infrared spectroscopy (fNIRS) signals were used as the input of the network. To avoid the over-fitting problem and effectively extract useful differentiation features from a sample with a very limited size (e.g., 25 ASDs and 22 TDs), a sliding window approach was utilized in which the initially recorded long-time (e.g., 480 s) time-series was divided into many partially overlapped short-time (7 s) sequences. By using this combined deep-learning network, a high accurate classification between ASD and TD could be achieved even with a single optical channel, e.g., 92.2% accuracy, 85.0% sensitivity, and 99.4% specificity. This result implies that the multilayer neural network CGRNN can identify characteristic features associated with ASD even in a short-time spontaneous hemodynamic fluctuation from a single optical channel, and second, the CGRNN can provide highly accurate prediction in ASD.

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Classification of autism children gait patterns using Neural Network and Support Vector Machine

Cited by 22 publications

References 25 publications

Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

Effects of Intra-Subject Variation in Gait Analysis on ASD Classification Performance in Machine Learning Models

Prediction in Autism by Deep Learning Short-Time Spontaneous Hemodynamic Fluctuations

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