Abstract
Given the impossibility of exposing trainees to hazardous scenarios for ethical, financial, and logistical reasons, virtual-environment (VE) based simulation training has been adopted in various safety-critical industries. Through simulation, participants can be exposed to a variety of training scenarios to assess their performance under different conditions. Along with performance measures, physiological signals may provide useful information about the trainee’s experience of the task. In this study, signals of the autonomic nervous system (ANS), specifically electrocardiogram (ECG), galvanic skin response (GSR), and respiration (RSP), were used to assess physiological arousal levels in 38 participants during 8 different conditions of an emergency evacuation task. On average, neutral and training conditions could be distinguished with a 79.4% average accuracy. In addition, arousal levels in different training scenarios were significantly different, and arousal level was negatively correlated with participant performance. This suggests ANS signals could be a useful measure of the scenario difficulty.
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The authors acknowledge with gratitude the support of the NSERC/Husky Energy Industrial Research Chair in Safety at Sea.
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Bui, S., Veitch, B., Power, S. (2019). Autonomic Nervous System Approach to Measure Physiological Arousal and Scenario Difficulty in Simulation-Based Training Environment. In: Ayaz, H., Mazur, L. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2018. Advances in Intelligent Systems and Computing, vol 775. Springer, Cham. https://doi.org/10.1007/978-3-319-94866-9_13
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