10th International Congress on Information and Communication Technology

Authors - Niyantra Mohan Babu, K.Vijayan, Alekhya Devi Malepati
Abstract - Autism spectrum disorder (ASD) is a developmental condition that affects social communication and behavioral intelligence. Many live out their lives in ignorance due to misdiagnosis or a lack of awareness. Individuals with high-functioning autism or Asperger’s Syndrome are hard to detect using a single method of detection because not every child will show the same symptoms. For example, a child with Asperger’s Syndrome with good eye contact but without social communication skills would be hard to detect using only their gaze points. This paper explores a method for the diagnosis of ASD through the integration of three models: gaze point tracking, quantitative behavioral checklist, and image processing. The eye-tracking technology pinpoints the coordinates of the gaze points and connects them to the training to detect whether the subject has ASD. Studies on this subject have shown discrepancies between detected and actual individuals with autism, as not all autistic children have an irregular gaze. (Yaneva et al. 2020) Insights into the child’s behavioral patterns are offered through Q-CHAT, which quantitatively categorizes the actions of the child. However, the Q-CHAT checklist is one-dimensional and differs in result as the child grows. (Howard et al. 2022) A pre-trained CNN VGG 16 model identifies traits in the children’s facial features as for the image processing model. But, not all individuals with autism have a facial structure that belies their disorder. (Anitha et al. 2024) This paper addresses all the problems through the novel integrated approach of ASD detection using multiple methods.