An IoT-Belief Rule Base Smart System to Assess Autism

Alam, Md. Eftekhar; Kaiser, M. Shamim; Hossain, Mohammad Shahadat; Andersson, Karl

doi:10.1109/ceeict.2018.8628131

Cited by 25 publications

(4 citation statements)

References 13 publications

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“…An IoT system was described by Alam et al [30]. ASD symptoms and indicators are gathered by ubiquitous sensor nodes in the Belief Rule Base (BRB) in order to categorize different autistic kid kinds.…”

Section: Children With Asd May Be Diagnosed and Theirmentioning

confidence: 99%

Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

et al. 2023

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The behaviors of children with autism spectrum disorder (ASD) are often erratic and difficult to predict. Most of the time, they are unable to communicate effectively in their own language. Instead, they communicate using hand gestures and pointing phrases. Because of this, it can be difficult for caregivers to grasp their patients’ requirements, although early detection of the condition can make this much simpler. Assistive technology and the Internet of Things (IoT) can alleviate the absence of verbal and nonverbal communication in the community. The IoT-based solutions use machine Learning (ML) and deep learning (DL) algorithms to diagnose and enhance the lives of patients. A thorough review of ASD techniques in the setting of IoT devices is presented in this research. Identifying important trends in IoT-based health care research is the primary objective of this review. There is also a technical taxonomy for organizing the current articles on ASD algorithms and methodologies based on different factors such as AI, SS network, ML, and IoT. On the basis of criteria such as accuracy and sensitivity, the statistical and operational analyses of the examined ASD techniques are presented.

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Section: Children With Asd May Be Diagnosed and Theirmentioning

confidence: 99%

Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

et al. 2023

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“…Internet of Things based ASD Detection [4], [116], [117], [100], [97], [80], [49], [78], [76], [92], [106], [77], [118], [91], [76], [48], [79], [39] 4.…”

Section: Asdmentioning

confidence: 99%

“…Md. Eftekhar Alam et al [118] designed the IoT-Belief Rule Base (BRB) system for ASD detection and used three sensors to measure the heart rate and EMG. This BRB system automatically collect the sign and symptom data of various autistic children in real time and classify the ASD children.…”

Section: Asdmentioning

confidence: 99%

A Systematic Survey of Technology Driven Diagnosis for ASD

Singh

Shukla

Gore

2022

Preprint

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Autism Spectrum Disorder (ASD), according to The Diagnostic and Statistical Manual of Mental Disorders- Fifth Edition (DSM-5), is a neurodevelopmental disorder that includes deficits of social interaction and social communication with the presence of restricted/repetitive behaviors. Due to communication deficits, ASD children have difficulties in joint attention and social reciprocity. Genetic disorders and/or environmental aspects are the leading causes of ASD. In 2021, the Centers for Disease Control and Prevention reported that approximately 1 among 44children in the U.S. is diagnosed with ASD. As per World Health Organization, ASD affects 14 out of every 10,000 children in Asian countries, which is approximately 23 children per 10,000 children in India. The Reported work shows that the early detection and intervention of ASD improve the language and communication deficiency of ASD children. This paper provides a comprehensive survey on various approaches to monitoring and detecting ASD children. Some monitoring and detection approaches covered in the study are clinical-based detection, MachineLearning -based ASD detection, Internet of Things (IoT)-based ASDdetection, Affective state detection, and Self Injurious Behaviour (SIB)detection of autistic children. This review also lists the several unaddressed challenges of early ASD detection. The purpose of this article is to enhance the understanding, provide guidelines for the domain, and provide insight into developing more practical and close-to-market products.

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“…Moreover, machine learning can be a valid biomarker-based technique that can contribute to objective ASD diagnosis [26]. Machine learning has also been applied in the Internet of Things (IoT) systems for ASD assessment [27,28]. Finally, regarding intervention, the quality of life of ASD people can be improved by assistive technology in the training of children with autism spectrum disorders [29].…”

Section: Introductionmentioning

confidence: 99%

The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review

et al. 2021

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Early and objective autism spectrum disorder (ASD) assessment, as well as early intervention are particularly important and may have long term benefits in the lives of ASD people. ASD assessment relies on subjective rather on objective criteria, whereas advances in research point to up-to-date procedures for early ASD assessment comprising eye-tracking technology, machine learning, as well as other assessment tools. This systematic review, the first to our knowledge of its kind, provides a comprehensive discussion of 30 studies irrespective of the stimuli/tasks and dataset used, the algorithms applied, the eye-tracking tools utilised and their goals. Evidence indicates that the combination of machine learning and eye-tracking technology could be considered a promising tool in autism research regarding early and objective diagnosis. Limitations and suggestions for future research are also presented.

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An IoT-Belief Rule Base Smart System to Assess Autism

Cited by 25 publications

References 13 publications

Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

A Systematic Survey of Technology Driven Diagnosis for ASD

The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review

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