Sensors, Vol. 19, Pages 4270: Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices
Sensors, Vol. 19, Pages 4270: Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices
Sensors doi: 10.3390/s19194270
Authors:
Kyle Ross
Pritam Sarkar
Dirk Rodenburg
Aaron Ruberto
Paul Hungler
Adam Szulewski
Daniel Howes
Ali Etemad
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 appli...
Source: Sensors - Category: Biotechnology Authors: Kyle Ross Pritam Sarkar Dirk Rodenburg Aaron Ruberto Paul Hungler Adam Szulewski Daniel Howes Ali Etemad Tags: Article Source Type: research
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