Abstract
Eye tracking is increasingly becoming prevalent for health-related interactive systems. Eye tracking can automatically reveal the presence of Central Field Loss (CFL), a dysfunctional visual behavior requiring time-intensive medical assessments. Since CFL typically results in poor fixation stability and more frequent saccades, this work investigates the use of machine learning to estimate the likelihood of CFL based on eye-movement data. We compared random forests, support vector machines, and long-short-term memory (LSTM) neural networks for their ability to discriminate between the presence or absence of an experimentally-induced CFL. We found that the estimation accuracy increases with larger samples of eye-tracking data. However, the computational costs outweigh any increase in accuracy after classifying window sizes of 1600 msec. Here, traditional machine learning approaches outperform the LSTM neural network. We discuss implications for continuous Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. MUM ’23, December 03–06, 2023, Vienna, Austria © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0921-0/23/12. . . $15.00 https://doi.org/10.1145/3626705.3627776 end-user CFL monitoring and processing power to provide an outlook for gaze-based wearable health devices in human-computer interaction.
