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Open AccessArticle

Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices

1
Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada
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Faculty of Engineering and Applied Sciences, Queen’s University, Kingston, ON K7L 3N6, Canada
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Department of Emergency Medicine, Kingston Health Sciences Centre, Kingston, ON K7L 2V7, Canada
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Department of Chemical Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada
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Department of Critical Care Medicine, Queen’s University, Kingston, ON K7L 2V7, Canada
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4270; https://doi.org/10.3390/s19194270
Received: 22 August 2019 / Revised: 26 September 2019 / Accepted: 28 September 2019 / Published: 1 October 2019
Simulation-based training has been proven to be a highly effective pedagogical strategy. However, misalignment between the participant’s level of expertise and the difficulty of the simulation has been shown to have significant negative impact on learning outcomes. To ensure that learning outcomes are achieved, we propose a novel framework for adaptive simulation with the goal of identifying the level of expertise of the learner, and dynamically modulating the simulation complexity to match the learner’s capability. To facilitate the development of this framework, we investigate the classification of expertise using biological signals monitored through wearable sensors. Trauma simulations were developed in which electrocardiogram (ECG) and galvanic skin response (GSR) signals of both novice and expert trauma responders were collected. These signals were then utilized to classify the responders’ expertise, successive to feature extraction and selection, using a number of machine learning methods. The results show the feasibility of utilizing these bio-signals for multimodal expertise classification to be used in adaptive simulation applications. View Full-Text
Keywords: adaptive simulation; machine learning; wearable device; affective computing adaptive simulation; machine learning; wearable device; affective computing
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Ross, K.; Sarkar, P.; Rodenburg, D.; Ruberto, A.; Hungler, P.; Szulewski, A.; Howes, D.; Etemad, A. Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices. Sensors 2019, 19, 4270.

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