Leveraging eye-tracking data to improve reliable detection of Alzheimer’s Disease and related patient’s states
Reliable detection of disease in the early stages of Alzheimer’s Disease (AD) continues to be a challenge. This project led by Drs. Conati and Field aims to investigate the value of eye-tracking data as one of the sources of information to build machine learning detectors of AD. In addition, the team will investigate eye-tracking based detectors of AD-related states such confusion and distress during naturally occurring tasks. This project aims to 1) validate the concept of using spontaneous speech and eye tracking data as clinical markers for early detection of AD through the use of machine-learning algorithms; and 2) investigate ways to increase detection accuracy by exploring both alternative machine learning settings, as well as alternative diagnostic tasks used for data collection. Eventually, the goal is to develop software that can detect states of confusion or distress in AD patients during day-to-day activities (e.g., reading an article) and automatically trigger interventions aimed at reducing the levels of discomfort and stress.