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Jesse W. Grootjen, Henrike Weingartner, Sven Mayer
Investigating the Effects of Eye-Tracking Interpolation Methods on Model Performance of LSTM
Proceedings of the 2024 Symposium on Eye Tracking Research and Applications (ETRA '24), Association for Computing Machinery, 2024-01-01 (bib)

Physiological sensing enables us to use advanced adaptive functionalities through physiological data (e.g., eye tracking) to change conditions. In this work, we investigate the impact of infilling methods on LSTM models' performance in handling missing eye tracking data, specifically during blinks and gaps in recording. We conducted experiments using recommended infilling techniques from previous work on an openly available eye tracking dataset and LSTM model structure. Our findings indicate that the infilling method significantly influences LSTM prediction accuracy. These results underscore the importance of standardized infilling approaches for enhancing the reliability and reproducibility of LSTM-based eye tracking applications on a larger scale. Future work should investigate the impact of these infilling methods in larger datasets to investigate generalizability.