Affiliations: [a] Amity Institute of Physiology and Allied Sciences Amity University, Noida, Uttar Pradesh, India | [b] INMAS, Defence Research and Development Organisation, New Delhi, India | [c] Amity Institute of Occupational Therapy, Amity University, Noida, Uttar Pradesh, India
Correspondence:
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Corresponding author: Deepti Ahuja, Research Scholar, Amity Institute of Physiology and Allied Sciences Amity University, Noida, Uttar Pradesh, India. E-mail: [email protected],[email protected].
Abstract: BACKGROUND: Autism Spectrum Disorder (ASD) is characterized by difficulties in social communication, social interactions, and repetitive behaviors. The earlier the diagnosis, the sooner the intervention therapy can begin, thus, making early diagnosis an important research goal. Technological innovations have tremendous potential to assist with early diagnosis and improve intervention programs. There is a limited understanding of the Wearable Technologies (WTs) currently available for autistic individuals, and how they measure functioning in this population. OBJECTIVE: The aim of this review is to identify advancement in wearable technologies used to monitor behavioral and physiological responses in the intervention of children with ASD. METHODS: A literature search was performed for English language articles and conference papers indexed in Science direct, PubMed, Web of science, Google scholar, ERIC, ProQuest and Cochrane in last five years (2017–2021), resulting in eighteen studies being reviewed. RESULTS: Wearable technology present a potential solution that can support and complement existing interventions. WTs measured a range of physiological and behavioural functions to objectively measure stereotypical motor movements, social function, communication, and emotion regulation in autistic youth in the context of a range of environments and activities. CONCLUSIONS: Advancement in technology that include behavioral and physiological response monitoring with wearable sensors provide the opportunity to acquire new understanding of the internal components of observable behavior of children with ASD. This could lead to better individualization of treatment and greater treatment effectiveness.
