MeltdownCrisis: Dataset of Autistic Children During Meltdown Crisis

Masmoudi, Marwa; Jarraya, Salma Kammoun; Hammami, Mohamed

doi:10.1109/sitis.2019.00048

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…Bibliographic coupling is a systematic quantitative methodology that, by its transparent nature, adds value compared to qualitative approaches ( 61 ). Shared citations among studies can provide a valuable indicator of interlinkages with the same topic ( 62 ). Figure 6 offers a network visualization of the bibliographic coupling of countries.…”

Section: Resultsmentioning

confidence: 99%

“…Technological advancements are expanding our understanding of autism and its underlying mechanisms, thereby improving support and life quality for ASD individuals. Machine learning and classification algorithms have improved diagnostic imaging and early autism diagnosis ( 62 , 85 ). MRI has detected brain differences in autistic infants ( 86 ), and eye-tracking technology is used to study eye movements and gaze patterns, identifying atypical visual attention and social interaction patterns associated with ASD ( 87 ).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Mapping autism’s research landscape: trends in autism screening and its alignment with sustainable development goals

Nedungadi,

Shah,

Stokes

et al. 2024

Front. Psychiatry

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IntroductionAutism Spectrum Disorder is a complex neurodevelopmental syndrome that profoundly affects social interactions, communication, and sensory perception. The research traced the evolution of autism research from 2011-2022, specifically focusing on the screening and diagnosis of children and students.MethodsThrough an analysis of 12,262 publications using the PRISMA framework, bibliographic coupling, science mapping, and citation analysis, this study illuminates the growth trajectory of ASD research and significant disparities in diagnosis and services.ResultsThe study indicates an increasing trend in autism research, with a strong representation of female authorship. Open Access journals show a higher average citation impact compared to their closed counterparts. A keyword co-occurrence analysis revealed four central research themes: Child Development and Support Systems, Early Identification and Intervention, Prevalence and Etiology, and Mental Health. The pandemic’s onset has prioritized research areas like mental health, telehealth, and service accessibility.DiscussionRecommendations on a global level stress the importance of developing timely biological markers for ASD, amplifying Disability Inclusion research, and personalizing mental health services to bridge these critical service gaps. These strategies, underpinned by interdisciplinary collaboration and telehealth innovation, particularly in low-resource settings, can offer a roadmap for inclusive, context-sensitive interventions at local levels that directly support SDG3’s aim for health and well-being for all.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Mapping autism’s research landscape: trends in autism screening and its alignment with sustainable development goals

Nedungadi,

Shah,

Stokes

et al. 2024

Front. Psychiatry

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show abstract

“…These videos registered different emotions and behaviors of a child during Meltdown crisis and in normal states witch built our ''MeltdownCrisis'' Dataset (table 1). The latter is describing in details in [19]. In their normal conditions autistic children can show facial expressions just like normal people but in their peculiar ways.…”

Section: Environment Settings and ''Meltdowncrisis'' Dataset Collmentioning

confidence: 99%

“…In [19], authors present the different emotions and attitudes expressed by autistic children both in a normal state and in a meltdown crisis. The recorded videos reveal different instances and detect a variety of compound emotions.…”

Section: Environment Settings and ''Meltdowncrisis'' Dataset Collmentioning

confidence: 99%

Compound Emotion Recognition of Autistic Children During Meltdown Crisis Based on Deep Spatio-Temporal Analysis of Facial Geometric Features

2020

Self Cite

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An important contribution to computer vision applications has been made by recognizing human emotion. Although it is very significant, this work considers the security of autistic people while in meltdown crisis by introducing a new system to warn caregivers through facial expressions detection. A precautionary approach has been taken to deal with meltdown crisis. Certainly, the indications of Meltdown are linked to abnormal facial expressions related to compound emotions. Actually, researchers thought long ago that Human Facial Expressions (HFE) are not able to express more than the seven basics emotions. HFE have been considered by psychologists as very complicated one, which can indicate two or even more emotions known as compound or mixed ones. A few studies have been done concerning Compound Emotion (CE). As well as, many difficult tasks to detect Compound Emotion Recognition (CER). In this paper, we empirically assess a group of deep spatio-temporal geometric features of microexpressions of autistic children during a meltdown crisis. To achieve this goal, we make a comparison of the CER performance and diverse collections of micro-expressions features to select the features which best differentiates autistic children CE in meltdown crisis from normal state, and the best classifier performance. We record autistic children videos in normal and meltdown crisis using Kinect camera in serious circumstances. The experimental evaluation shows that the deep spatio-temporal geometric features and Recurrent Neural Network RNN with 3 hidden layer using Information Gain Feature Selection methods provide best performance (85.8%). INDEX TERMS Autism, deep spatio-temporal features, meltdown crisis, facial expressions, compound emotions.

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“…Specifically, Convolutional Neural Networks (CNNs) have proven highly capable of extracting features based on spatio-temporal descriptors to classify various gestures exhibited by ASD children. However, existing models have limitations, as they fail to compute the appearance of multisite meltdowns and fully exploit the interdependence among these meltdowns for severity computation in the acquired facial images of children, hindering the development of a comprehensive grading system [26]. This paper presents a novel approach, introducing a Long Short-Term Memory (LSTM) integrated Convolutional Neural Network designed to detect multisite meltdowns and exploit their interdependence for severity computation in ASD.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Autism Severity Classification: Integrating LSTM into CNNs for Multisite Meltdown Grading

Alam,

Raja,

Gulzar

et al. 2023

IJACSA

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Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction, verbal and non-verbal communication, and is often associated with cognitive and neurobehavioral challenges. Timely screening and diagnosis of ASD are crucial for early educational planning, treatment, family support, and timely medical intervention. Manual diagnostic methods are time-consuming and labor-intensive, underscoring the need for automated approaches to assist caretakers and parents. While various researchers have employed machine learning and deep learning techniques for ASD diagnosis, existing models often fall short in capturing the complexity of multisite meltdowns and fully leveraging the interdependence among these meltdowns for severity assessment in acquired facial images of children, hindering the development of a comprehensive grading system. This paper introduces a novel approach using a Long Short Term Memory (LSTM) integrated Convolution Neural Network (CNN) designed to identify multisite meltdowns and exploit their interdependence for severity assessment in ASD. The process begins with image pre-processing, involving discrete convolution filters for noise removal and contrast enhancement to improve image quality. The enhanced image then undergoes instance segmentation using the Segment Anything model to identify significant regions in the child's image. The segmented region is subjected to principal component analysis for feature extraction, and these features are utilized by the LSTM-integrated CNN for meltdown detection and severity classification. The model is trained using children's images extracted from videos, and testing is performed on videos captured during children's observations. Performance analysis reveals superior results, with a training accuracy of 88% and validation accuracy of 84%, outperforming conventional methods. This innovative approach not only enhances the efficiency of ASD diagnosis but also provides a more nuanced understanding of multisite meltdowns and their impact on severity, contributing to the development of a robust grading system.

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MeltdownCrisis: Dataset of Autistic Children During Meltdown Crisis

Cited by 7 publications

References 17 publications

Mapping autism’s research landscape: trends in autism screening and its alignment with sustainable development goals

Mapping autism’s research landscape: trends in autism screening and its alignment with sustainable development goals

Compound Emotion Recognition of Autistic Children During Meltdown Crisis Based on Deep Spatio-Temporal Analysis of Facial Geometric Features

Enhancing Autism Severity Classification: Integrating LSTM into CNNs for Multisite Meltdown Grading

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