A Survey on the Application of the Internet of Things in the Diagnosis of Autism Spectrum Disorder

Arbaoui, Fatima Ez Zahra El; Hari, Kaoutar El; Saidi, Rajaa

doi:10.1007/978-3-030-94188-8_4

Cited by 4 publications

(5 citation statements)

References 45 publications

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“…These insights have deepened our comprehension of social cognition impairments in autism, aiding early identification and interventions. Other research papers on the use of machine learning methods for classifying autism include [167][168][169][170][171][172][173][174][175][176][177][178][179].…”

Section: Hubel and Wiesel's Contributionmentioning

confidence: 99%

“…Individuals with autism face challenges in recognizing faces, with disruptions in neural systems linked to social issues. [165,166] Machine learning methods for classifying autism [167][168][169][170][171][172][173][174][175][176][177][178][179] 5.2. Van Essen's Functional Mapping of the Brain Van Essen's work on brain mapping serves as a bridge between our brain's complex networks and advanced deep neural networks (DNNs) in modern AI.…”

Section: Summary Referencesmentioning

confidence: 99%

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A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

Manakitsa,

Maraslidis,

Moysis

et al. 2024

Technologies

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Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications.

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Section: Hubel and Wiesel's Contributionmentioning

confidence: 99%

Section: Summary Referencesmentioning

confidence: 99%

A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

Manakitsa,

Maraslidis,

Moysis

et al. 2024

Technologies

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show abstract

“…However, this categorization is insu cient since mechatronic devices can assume a key role in many other elds, such as education and developing social skills. This work is reated to the two reviews presented in [44] and [45]. Prevously, we analyzed how IoT can be deployed for autism.…”

Section: Introductionmentioning

confidence: 99%

“…The rst category was traited in [44], the work presented in [45] consider the second category, in this review we adresse the third category and we explore IoT approaches that can be used by people with autism themselves.…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review on the internet of things applications used by people with autism spectrum disorder

ARBAOUI

ELHARI

SAIDI

2023

Preprint

Self Cite

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In this paper, we provide a systematic literature review that explores the Internet of Things (IoT) application to support people affected by Autism Spectrum Disorder (ASD). This disorder represents several limitations to learning, interacting with others, and communicating. Research has shown the great potential of the IoT in providing approaches that target individuals with ASD and help them in learning and their daily activities. Several studies on the topic of IoT were reviewed. The main aim of this review is to recognize relevant studies about the use of IoT to support people affected by ASD, particularly children. In addition, a classification is presented to classify the existing papers.

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“…In early infancy, such indications begin manifesting themselves, affecting social communication and interaction in addition to adaptive development and academic performance [1]. Concerning social and interpersonal interaction, children with ASD have difficulties and hurdles in nonverbal and verbal communication [2]. They even tend to engage in repeated behavior.…”

Section: Introductionmentioning

confidence: 99%

Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

Thanarajan,

Alotaibi,

Rajendran

et al. 2023

Computers, Materials &Amp; Continua

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Autism spectrum disorder (ASD) can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics, like changes in behavior, social disabilities, and difficulty communicating with others. Eye tracking (ET) has become a useful method to detect ASD. One vital aspect of moral erudition is the aptitude to have common visual attention. The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection. Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD, but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection. It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism. The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking. The Chaotic Butterfly Optimization technique is used to identify this specific disturbance. Therefore, this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning (ETASD-CBODL) technique. The presented ETASD-CBODL technique mainly focuses on the recognition of ASD via the ET and DL models. To accomplish this, the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS. In addition, the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization. Finally, the long-shortterm memory (LSTM) model is exploited for the recognition and classification of ASD. To assess the performance of the ETASD-CBODL technique, a series of simulations were performed on datasets from the figure-shared data repository. The experimental values of accuracy (99.29%), precision (98.78%), sensitivity (99.29%) and specificity (99.29%) showed a better performance in the ETASD-CBODL technique over recent approaches.

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A Survey on the Application of the Internet of Things in the Diagnosis of Autism Spectrum Disorder

Cited by 4 publications

References 45 publications

A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

A systematic literature review on the internet of things applications used by people with autism spectrum disorder

Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

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