Integrating saccade metrics into a wearable assistive device

This project will explore how eye movements can be used to improve assistive Augmented Reality (AR) devices for people with motor impairments. Many AR systems rely on head, hand, or body movements to make choices, but this is not always possible for individuals with limited mobility. However, eye movement is often preserved, making it a powerful way to both gather information and signal decisions. The challenge is that people use their eyes not just to choose, but also to look around and think, so systems must be able to tell the difference between exploring and deciding. The research will focus on identifying and testing features from eye movement patterns that can predict when a user has shifted from looking around to making a decision. These features will be combined with existing brain-signal (EEG) systems to create a more accurate and responsive decision-prediction tool. For the partner organization, this work will help advance the development of AR technologies that are easier and more natural to use, improving accessibility and independence for people with motor disabilities.

Faculty Supervisor:

Mazyar Fallah

Student:

Partner:

Cognixion Inc.

Discipline:

Life Sciences

Sector:

Manufacturing

University:

University of Guelph

Program: