Classification of flight phases based on pilots' visual scanning strategies
Spencer, S.N. (editor)
Eye movements analysis has great potential for understanding operator behaviour in many safety-critical domains, including aviation. In addition to traditional eye-tracking measures on pilots’ visual behavior, it seems promising to incorporate machine learning approaches to classify pilots’ visual scanning patterns. However, given the multitude of pattern measures, it is unclear which are better suited as predictors. In this study we analyzed the visual behaviour of eight pilots, flying different flight phases in a moving-base flight simulator. With this limited dataset we present a methodological approach to train linear Support Vector Machine models, using different combinations of the attention ratio and scanning pattern features. The results show that the overall accuracy to classify the pilots’ visual behaviour in different flight phases, improves from 51.6% up to 64.1% when combining the attention ratio and instrument scanning sequence in the classification model.
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Support vector machines
Eye movement analysis
Association for Computing Machinery
Eye Tracking Research and Applications Symposium (ETRA), 2022 ACM Symposium on Eye Tracking Research and Applications, ETRA 2022, 8 June 2022 through 11 June 2022