Smartphone-based pupil dynamics analysis for neurodevelopmental disorder diagnosis

Abstract

This research aims to improve the diagnosis of neurodevelopmental disorders by using smartphones, focusing on the psychology of eyes through pupil dynamics analysis. We are developing a user-friendly smartphone app to track and analyze changes in pupil size and reactivity, considering the eyes as a unique window into cognitive and emotional states. The technology provides a non-intrusive and accessible way to detect neurodevelopmental disorders early. Pupil dynamics, connected to arousal, attention, and emotions, offer insights into the psychological aspects of individuals navigating these disorders. Our method involves collecting and validating datasets, ensuring the psychological reliability of the data by drawing insights from existing research papers. Using machine learning and building on an already published dataset, we aim to create a model that uses artificial intelligence to classify and predict neurodevelopmental disorders based on analyzed pupil dynamics. This approach not only enhances diagnostic accuracy but also explores the psychological intricacies associated with these disorders. By combining smartphone technology and machine learning, our research seeks to offer a practical and cost-effective solution for early diagnosis. This approach has the potential to impact healthcare practices, providing a more inclusive and timely method for identifying neurodevelopmental disorders. Our multidisciplinary effort aims to bridge the gap between the physiological and psychological aspects of visual communication, opening new avenues for therapeutic interventions and understanding.