Identifying Eye Movement Patterns for An Adaptive Approach to Correcting Eye Tracking Data in Reading Tasks

No single eye-tracking correction algorithm is universally effective across all reading patterns and distortions. Yet, a single algorithm is often chosen to correct entire datasets, leading to non-optimal correction performance. In this paper, we present an adaptive approach that dynamically selects the optimal algorithm for each trial, significantly enhancing correction accuracy. The proposed adaptive approach relies on identifying the eye movement patterns in each trial, and based on the combination of patterns present the optimal algorithm is used to correct the trial. We assess this approach with synthetic data and four real datasets with a total of 152 trials, comparing the results to 15 algorithms. Our results confirm that none of the previous algorithms is able to handle all reading patterns and distortions. However, the results show that the proposed approach is the best performing in simulated trials and real data.