Aarya – A Kinesthetic companion for children with Autism Spectrum Disorder

Sreedasyam, Rachita; Rao, Aishwarya Govinda; Sachidanandan, Nidhi; Sampath, Nalini; Vasudevan, Shriram K.

doi:10.3233/jifs-169240

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…A UTISM Spectrum Disorder (ASD) is a neurodevelopmental condition associated with brain development that starts early stage of life, impacting a person's social relationships and interaction issues [1], [2]. ASD has restricted and repeated behavioral patterns, and the word spectrum encompasses a wide range of symptoms and intensity [3]- [5]. Even though there is no sustainable solution for ASD, simply early intervention and proper medical care will make a significant difference in a kid's development to focus on improving a child's behaviors and skills in communication [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

et al. 2023

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Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder that affects the everyday life of affected patients. Though it is considered hard to completely eradicate this disease, disease severity can be mitigated by taking early interventions. In this paper, we propose an effective framework for the evaluation of various Machine Learning (ML) techniques for the early detection of ASD. The proposed framework employs four different Feature Scaling (FS) strategies i.e., Quantile Transformer (QT), Power Transformer (PT), Normalizer, and Max Abs Scaler (MAS). Then, the feature-scaled datasets are classified through eight simple but effective ML algorithms like Ada Boost (AB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Our experiments are performed on four standard ASD datasets (Toddlers, Adolescents, Children, and Adults). Comparing the classification outcomes using various statistical evaluation measures (Accuracy, Receiver Operating Characteristic: ROC curve, F1-score, Precision, Recall, Mathews Correlation Coefficient: MCC, Kappa score, and Log loss), the best-performing classification methods, and the best FS techniques for each ASD dataset are identified. After analyzing the experimental outcomes of different classifiers on feature-scaled ASD datasets, it is found that AB predicted ASD with the highest accuracy of 99.25%, and 97.95% for Toddlers and Children, respectively and LDA predicted ASD with the highest accuracy of 97.12% and 99.03% for Adolescents and Adults datasets, respectively. These highest accuracies are achieved while scaling Toddlers and Children with normalizer FS and Adolescents and Adults with the QT FS method. Afterward, the ASD risk factors are calculated, and the most important attributes are ranked according to their importance values using four different Feature Selection Techniques (FSTs) i.e., Info Gain Attribute Evaluator (IGAE), Gain Ratio Attribute Evaluator (GRAE), Relief F Attribute Evaluator (RFAE), and Correlation Attribute Evaluator (CAE). These detailed experimental evaluations indicate that proper finetuning of the ML methods can play an essential role in predicting ASD in people of different ages. We argue that the detailed feature importance analysis in this paper will guide the decision-making of healthcare practitioners while screening ASD cases. The proposed framework has achieved promising results compared to existing approaches for the early detection of ASD.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

et al. 2023

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“…It receives gesture triggers and dynamically adapts to interaction patterns of participants facilitating eye contact. In [19], some interesting ideas for an interactive social companion for children with Autism Spectrum Disorder (ASD), called Aarya, are presented. The tool provides gesture-based virtual environment and helps children to improve their confidence in forming relationships and interacting with other people.…”

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confidence: 99%

Special issue on soft computing and intelligent systems: Tools, techniques and applications

Thampi

El-Alfy

2017

IFS

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This special issue is a selected collection of papers submitted to the Second International Symposium on Intelligent Systems Technologies and Applications (ISTA'16), September 21-24, 2016, Jaipur, India. These papers have been reviewed and accepted for presentation at the symposium and for publication in the Journal of Intelligent & Fuzzy Systems (JIFS). In this special issue there are 44 papers covering a wide range of tools, techniques and applications of soft computing and intelligent systems.The International Symposium on Intelligent Systems Technologies and Applications aims to bring together researchers in related fields to explore and discuss various aspects of intelligent systems technologies and their applications. It provides excellent opportunities for the presentation of interesting new research results and discussion about them, leading to knowledge transfer and generation of new ideas.There are seven papers addressing research problems in bioinfomratics and medical research. In c-means (FCM) algorithm is proposed and evaluated on publicly available gene expression datasets. The results show that even with a few labeled samples, accurate prediction of cancer subtypes can be attained. In [2], a new texture-based feature extraction algorithm is proposed for extracting relevant and informative features from brain MR images having tumor. It is based on finding the texture description using nine different variants of texture objects and forming an intermediate texture index matrix using texture objects with high-pass and low-pass spiral filters. The resultant two-index matrix is used to generate the Texture Co-occurrence Matrix which helps to extract spatial and spectral domain features for brain MRI classification. Experiments on a dataset of 660 T1-weighted post contrast brain MR images with five different types of malignant tumors show that the proposed method can lead to significant results in abnormality classification when compared with the state-of-the-art GLCM and Run-length algorithms. In [3], a novel method is proposed for early detection and diagnosis of breast cancer in digital mammograms. The proposed technique uses Particle Swarm Optimization (PSO), Cellular Neural Network (CNN) and Probabilistic Neural Network (PNN). The breast mass texture feature extraction is carried out using Gray-Level Co-occurrence Matrix (GLCM)

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Healthcare Monitoring System Based on IoT

Krishna

Sampath

2017

2017 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)

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Aarya – A Kinesthetic companion for children with Autism Spectrum Disorder

Cited by 8 publications

References 5 publications

A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

Special issue on soft computing and intelligent systems: Tools, techniques and applications

Healthcare Monitoring System Based on IoT

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