Mind Your Vision: Multimodal Estimation of Refractive Disorders Using Electrooculography and Eye Tracking
Journal:
arXiv
Published Date:
May 24, 2025
Abstract
Refractive errors are among the most common visual impairments globally, yet
their diagnosis often relies on active user participation and clinical
oversight. This study explores a passive method for estimating refractive power
using two eye movement recording techniques: electrooculography (EOG) and
video-based eye tracking. Using a publicly available dataset recorded under
varying diopter conditions, we trained Long Short-Term Memory (LSTM) models to
classify refractive power from unimodal (EOG or eye tracking) and multimodal
configuration. We assess performance in both subject-dependent and
subject-independent settings to evaluate model personalization and
generalizability across individuals. Results show that the multimodal model
consistently outperforms unimodal models, achieving the highest average
accuracy in both settings: 96.207\% in the subject-dependent scenario and
8.882\% in the subject-independent scenario. However, generalization remains
limited, with classification accuracy only marginally above chance in the
subject-independent evaluations. Statistical comparisons in the
subject-dependent setting confirmed that the multimodal model significantly
outperformed the EOG and eye-tracking models. However, no statistically
significant differences were found in the subject-independent setting. Our
findings demonstrate both the potential and current limitations of eye movement
data-based refractive error estimation, contributing to the development of
continuous, non-invasive screening methods using EOG signals and eye-tracking
data.
