A Systematic Survey of Technology Driven Diagnosis for ASD

Abstract

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.