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<p>Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that includes deficits in social communication and social interaction, with the presence of restricted and repetitive behaviors. Children with ASD often struggle with joint attention and social reciprocity due to communication deficits. Genetic disorders and environmental factors are the primary causes of ASD. According to the Centers for Disease Control and Prevention (CDC) in 2021, ASD affected 1 in 44 children in the United States. The World Health Organization (WHO) reports that the prevalence of ASD among Asian children is 14 per 10,000, with a higher incidence of 23 per 10,000 in India. ASD has no cure, so early diagnosis is important for reducing problems related to social interaction and communication deficits. Early detection of ASD is challenging because its symptoms and severity levels vary widely from child to child. Research has shown that children with ASD can benefit significantly from early detection and intervention. Most surveys focused on ASD detection, but the affective states and self injurious behaviors of autistic children are less explored. This paper provides a systematic survey of ASD in the context of machine learning and Internet of Things (IoT) devices, focusing on three specific areas of autism: ASD detection, affective state detection, and self injurious behavior detection. We focus on clinical, machine learning, and IoT methods for ASD detection. Additionally, the paper examines self injurious behavior detection and affective state detection in autistic children, including the prevalence, causes, and possible interventions, as well as provides information on mobile applications used in ASD diagnosis. The purpose of the paper is to identify research limitations and gaps in the literature and propose future directions to detect ASD. In general, this article emphasizes the importance of increasing awareness and resources for the ASD community and provides valuable information for healthcare professionals, researchers, and product developers.</p>

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A Systematic Survey of Technology Driven Diagnosis for Autism Spectrum Disorder

  • 期刊论文

  • 发表日期：

    2025年10月3日

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  • Uday Singh,
  • Shailendra Shukla,
  • Manoj Madhava Gore

摘要

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that includes deficits in social communication and social interaction, with the presence of restricted and repetitive behaviors. Children with ASD often struggle with joint attention and social reciprocity due to communication deficits. Genetic disorders and environmental factors are the primary causes of ASD. According to the Centers for Disease Control and Prevention (CDC) in 2021, ASD affected 1 in 44 children in the United States. The World Health Organization (WHO) reports that the prevalence of ASD among Asian children is 14 per 10,000, with a higher incidence of 23 per 10,000 in India. ASD has no cure, so early diagnosis is important for reducing problems related to social interaction and communication deficits. Early detection of ASD is challenging because its symptoms and severity levels vary widely from child to child. Research has shown that children with ASD can benefit significantly from early detection and intervention. Most surveys focused on ASD detection, but the affective states and self injurious behaviors of autistic children are less explored. This paper provides a systematic survey of ASD in the context of machine learning and Internet of Things (IoT) devices, focusing on three specific areas of autism: ASD detection, affective state detection, and self injurious behavior detection. We focus on clinical, machine learning, and IoT methods for ASD detection. Additionally, the paper examines self injurious behavior detection and affective state detection in autistic children, including the prevalence, causes, and possible interventions, as well as provides information on mobile applications used in ASD diagnosis. The purpose of the paper is to identify research limitations and gaps in the literature and propose future directions to detect ASD. In general, this article emphasizes the importance of increasing awareness and resources for the ASD community and provides valuable information for healthcare professionals, researchers, and product developers.