College - Author 1
College of Engineering
Department - Author 1
Computer Science Department
Advisor
Ria, Kanjilal, College of Engineering, Computer Engineering Department
Funding Source
The project is supported by ODIN diagonistics
Date
10-2025
Abstract/Summary
This research project proposes the development of a novel, data-driven framework for detecting concussions using machine learning (ML) and deep learning (DL) models applied to high-resolution eye-tracking data. Unlike traditional concussion assessments that rely on subjective evaluations, this work seeks to identify objective, quantifiable biomarkers derived from ocular dynamics—such as saccadic velocity, smooth pursuit accuracy, and pupillary response—captured through advanced eye-tracking technology. The project will explore state-of-the-art feature extraction techniques and predictive modeling approaches to uncover subtle neuro-ocular signatures associated with mild traumatic brain injury. By combining principles from biomedical signal processing, artificial intelligence, and neurophysiology, this work advances current understanding of how concussions manifest in eye movement patterns and contributes to the interdisciplinary development of intelligent diagnostic systems. The outcomes have the potential to transform clinical and sideline concussion screening by enabling more accurate, timely, and scalable detection methods grounded in computational neuroscience.
October 1, 2025.
Included in
URL: https://digitalcommons.calpoly.edu/ceng_surp/117