We can easily monitor physiological signals like heart rate and respiration to track physical diseases using wearable sensors, but what about tracking decline in attention, memory and other cognitive skills that can occur with neurodegenerative diseases like Parkinson’s? This project aims to measure and model looking behavior during daily life to track cognitive decline in Parkinson’s patients. Why “looking behavior”? Because, paying attention to where one looks, can reveal quite a lot about what they may be thinking. We will use deep-learning with a transformer architecture to predict where Parkinson’s patients will look next based on what they are looking at and their previous fixations. We expect that models built on different types of Parkinson’s patients and control groups will be able to differentiate subtle differences in looking behaviors. The long-term goal of the project is to use looking behavior modeling as a foundation for minimally-invasive and sensitive measures for diagnosing and tracking neurodegenerative diseases.