2024/01/07 by Md. Farhadul Islam, Meem Arafat Manab, Islam, Md. Farhadul +13 · 1 citation
Computer Science · Neuroscience · #Autism Spectrum Disorder Research #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Gaze Tracking and Assistive Technology #Image and Video Processing (eess.IV) #Neuroscience and Neural Engineering #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2401.03575
openalex publication_date 2024/01/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Autism Spectrum Disorder (ASD) is a complicated neurological condition which is challenging to diagnose. Numerous studies demonstrate that children diagnosed with autism struggle with maintaining attention spans and have less focused vision. The eye-tracking technology has drawn special attention in the context of ASD since anomalies in gaze have long been acknowledged as a defining feature of autism in general. Deep Learning (DL) approaches coupled with eye-tracking sensors are exploiting additional capabilities to advance the diagnostic and its applications. By learning intricate nonlinear input-output relations, DL can accurately recognize the various gaze and eye-tracking patterns and adjust to the data. Convolutions alone are insufficient to capture the important spatial information in gaze patterns or eye tracking. The dynamic kernel-based process known as involutions can improve the efficiency of classifying gaze patterns or eye tracking data. In this paper, we utilise two different image-processing operations to see how these processes learn eye-tracking patterns. Since these patterns are primarily based on spatial information, we use involution with convolution making it a hybrid, which adds location-specific capability to a deep learning model. Our proposed model is implemented in a simple yet effective approach, which makes it easier for applying in real life. We investigate the reasons why our approach works well for classifying eye-tracking patterns. For comparative analysis, we experiment with two separate datasets as well as a combined version of both. The results show that IC with three involution layers outperforms the previous approaches.