Predicting Cognitive Load in an Emergency Simulation Based on Behavioral and Physiological Measures
| Autor: | Peter Gerjets, Franz Wortha, Natalia Sevcenko, Tobias Appel, Enkelejda Kasneci, Korbinian Moeller, Katerina Tsarava, Manuel Ninaus |
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| Rok vydání: | 2019 |
| Předmět: |
Normalization (statistics)
Emergency personnel business.industry Computer science 05 social sciences Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine Similarity (psychology) Pupillary response Eye tracking 0501 psychology and cognitive sciences Artificial intelligence business computer Simulation based 050107 human factors 030217 neurology & neurosurgery Cognitive load |
| Zdroj: | ICMI |
| Popis: | The reliable estimation of cognitive load is an integral step towards real-time adaptivity of learning or gaming environments. We introduce a novel and robust machine learning method for cognitive load assessment based on behavioral and physiological measures in a combined within- and cross-participant approach. 47 participants completed different scenarios of a commercially available emergency personnel simulation game realizing several levels of difficulty based on cognitive load. Using interaction metrics, pupil dilation, eye-fixation behavior, and heart rate data, we trained individual, participant-specific forests of extremely randomized trees differentiating between low and high cognitive load. We achieved an average classification accuracy of 72%. We then apply these participant-specific classifiers in a novel way, using similarity between participants, normalization, and relative importance of individual features to successfully achieve the same level of classification accuracy in cross-participant classification. These results indicate that a combination of behavioral and physiological indicators allows for reliable prediction of cognitive load in an emergency simulation game, opening up new avenues for adaptivity and interaction. |
| Databáze: | OpenAIRE |
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