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

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 a...

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Detalles Bibliográficos
Autores principales: Ross, Kyle, Sarkar, Pritam, Rodenburg, Dirk, Ruberto, Aaron, Hungler, Paul, Szulewski, Adam, Howes, Daniel, Etemad, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806062/
https://www.ncbi.nlm.nih.gov/pubmed/31581563
http://dx.doi.org/10.3390/s19194270
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author Ross, Kyle
Sarkar, Pritam
Rodenburg, Dirk
Ruberto, Aaron
Hungler, Paul
Szulewski, Adam
Howes, Daniel
Etemad, Ali
author_facet Ross, Kyle
Sarkar, Pritam
Rodenburg, Dirk
Ruberto, Aaron
Hungler, Paul
Szulewski, Adam
Howes, Daniel
Etemad, Ali
author_sort Ross, Kyle
collection PubMed
description 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.
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spelling pubmed-68060622019-11-07 Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices Ross, Kyle Sarkar, Pritam Rodenburg, Dirk Ruberto, Aaron Hungler, Paul Szulewski, Adam Howes, Daniel Etemad, Ali Sensors (Basel) Article 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. MDPI 2019-10-01 /pmc/articles/PMC6806062/ /pubmed/31581563 http://dx.doi.org/10.3390/s19194270 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ross, Kyle
Sarkar, Pritam
Rodenburg, Dirk
Ruberto, Aaron
Hungler, Paul
Szulewski, Adam
Howes, Daniel
Etemad, Ali
Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices
title Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices
title_full Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices
title_fullStr Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices
title_full_unstemmed Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices
title_short Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices
title_sort toward dynamically adaptive simulation: multimodal classification of user expertise using wearable devices
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806062/
https://www.ncbi.nlm.nih.gov/pubmed/31581563
http://dx.doi.org/10.3390/s19194270
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