Early Screening of ASD Based on Hand Gesture Analysis

Abstract

Neurodevelopmental disorder refers to behavioral and cognitive impairment during development, which is manifested as significant difficulties in intelligence, motor or social skills. Among these disorders, autism spectrum disorder (ASD), language disorder (LD) and mental retardation (MR) are easily misclassified due to their similar symptoms, which can be very detrimental to later treatment. Therefore, it is important to screen and classify these types of patients correctly. Traditionally, diagnosis of these disorders has relied on the American Psychiatric Research Association’s DSM-5, but these methods require professionals and special assessments to diagnose patients, which can take a lot of time and cost. Nowadays, thanks to the development of deep learning, many researches have used some biological signals (such as electroencephalogram, facial expression and gesture, etc.) for the early diagnosis of neurodevelopmental diseases and have achieved good results. In this work, we proposed a method based on deep learning, which can well distinguish different types of patients only by children’s hand gestures. In order to verify the effectiveness of the method, we conducted a series of experiments on the TASD dataset, and finally the classification accuracy of these three types of patients reached 99.42%. It proves that using only hand gestures is also effective in the screening of ASD, LD, and MR.