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Exploring the Suitability of Using Virtual Reality and Augmented Reality for Anatomy Training
IEEE Transactions on Human-Machine Systems ; : 2023/12/01 00:00:00.000, 2023.
Article in English | Scopus | ID: covidwho-2235423
ABSTRACT
Research on alternative ways to provide anatomy learning and training has increased over the past few years, especially since the COVID-19 pandemic. Virtual reality (VR) and augmented reality (AR) represent two promising alternatives in this regard. For this reason, in this work, we analyze the suitability of applying VR and AR for anatomy training, comparing an optical-based AR setup and a semi-immersive setup based on a VR table, using the same anatomy training software and the same interaction system. The AR-based setup uses a Magic Leap One, whereas the VR table is configured through the use of stereoscopic TV displays and a motion-capture system. This experiment builds on a previous one (Vergel et al., 2020) on which we have improved the AR-based setup and increased the complexity of one of the two tasks. The goal of this new experiment is to confirm whether the changes made in the setups modify the previous conclusions. Our hypothesis is that the improved AR-based setup will be more suitable, for anatomy training, than the VR-based setup. For this reason, we conducted an experimental research with 45 participants, comparing the use of an anatomy training software. Objective and subjective data were collected. The results show that the AR-based setup is the preferred choice. The differences in measurable performance were small but also favorable to the AR setup. In addition, participants provided better subjective ratings for the AR-based setup, confirming our initial hypothesis. Nevertheless, both setups offer a similar overall performance and provide excellent results in the subjective measures, with both systems approaching the highest possible values. IEEE
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: IEEE Transactions on Human-Machine Systems Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: IEEE Transactions on Human-Machine Systems Year: 2023 Document Type: Article