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Investigating the Effects of Eye-Tracking Interpolation Methods on Model Performance of LSTM

MCML Authors

Jesse Grootjen

→ Group Albrecht Schmidt
Human-Centered Ubiquitous Media

Sven Mayer

Prof. Dr.

Associate

* Former Associate

Abstract

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.

inproceedings

PETMEI @ETRA 2024

9th International Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction at the ACM Symposium on Eye Tracking Research and Applications. Glasgow, Scotland, Jun 04-07, 2024.