Mind Your Vision: Multimodal Estimation of Refractive Disorders Using Electrooculography and Eye Tracking

TL;DR

This study investigates a passive, multimodal approach using electrooculography and eye tracking data with LSTM models to estimate refractive errors, showing high accuracy in personalized settings but limited generalization across individuals.

Contribution

It introduces a multimodal eye movement-based method for refractive error estimation and evaluates its performance in both personalized and generalized contexts.

Findings

Multimodal models outperform unimodal ones in subject-dependent accuracy.

Achieved 96.2% accuracy in personalized refractive error classification.

Limited generalization, with only 8.88% accuracy in subject-independent tests.

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.