MIT EECS | CS+HASS Undergraduate Research and Innovation Scholar
Project: Using Eye Tracking and Pen Movement to Detect Solution Strategies For Maze Solving
A simple pen and paper task, called the Clock Drawing Test, has been used for more than 50 years as an accepted method for detecting cognitive impairment, such as that seen with Alzheimer’s, Parkinson’s, and other neurological disorders. Decades were spent improving this test using a digital pen and machine learning. The intent of this project is to improve upon this detection by combing eye tracking with the existing pen tracking then analyzing how subjects think about a maze as they solve it. Using data gathered from local volunteers and subjects with impairments at the Lahey Clinic, we will use data display and exploration tools, such as combined playback, to detect solution strategies used by subjects.
I’d like to use SuperUROP as an opportunity to gain a better understanding of high-level research methods. I already have some experience applying machine learning to open-ended research questions, attempting to improve educational curricula using software tools to model mathematical representations. I am excited to expand my practical application of machine learning and modelling techniques to analyze human thought processes.