Abstract
This paper presents a multimodal affect assessment protocol developed for aviation training, which consists of physiological, behavioral measures of affect and subjective self-report of affective correlates. Data convergence is examined by comparing physiological and behavioral data output with self-report variables. We found significant correlations between arousal inferred from electro-dermal activity (EDA) and self-reported workload, fatigue and effort. We also found that the intensities of emotions inferred from facial expression correlate with self-reported variables. These findings support the validity of EDA and facial expression as measures of affect in aviation training context.
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Acknowledgement
This study is jointly funded by Natural Sciences and Engineering Research Council of Canada (514052-17), Consortium for Aerospace Research and Innovation in Canada (CARIC) and Quebec (CRIAQ) (OPR-1618). We thank Alain Bourgon and Hugh Grenier (CAE Inc.) for proposing and configuring the X-plane environment and tasks, and Maher Chaouachi (CAE Inc.) for proposing the EDA analysis algorithm. We thank all collaborators from the InLook project which this study is part of.
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Li, T., Jraidi, I., Ruiz Segura, A., Holton, L., Lajoie, S. (2020). Developing a Multimodal Affect Assessment for Aviation Training. In: Kumar, V., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science(), vol 12149. Springer, Cham. https://doi.org/10.1007/978-3-030-49663-0_5
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