| Autor: |
Christian Rack, Tamara Fernando, Murat Yalcin, Andreas Hotho, Marc Erich Latoschik |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Frontiers in Virtual Reality, Vol 4 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2673-4192 |
| DOI: |
10.3389/frvir.2023.1272234 |
| Popis: |
Introduction: This paper addresses the need for reliable user identification in Extended Reality (XR), focusing on the scarcity of public datasets in this area.Methods: We present a new dataset collected from 71 users who played the game “Half-Life: Alyx” on an HTC Vive Pro for 45 min across two separate sessions. The dataset includes motion and eye-tracking data, along with physiological data from a subset of 31 users. Benchmark performance is established using two state-of-the-art deep learning architectures, Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU).Results: The best model achieved a mean accuracy of 95% for user identification within 2 min when trained on the first session and tested on the second.Discussion: The dataset is freely available and serves as a resource for future research in XR user identification, thereby addressing a significant gap in the field. Its release aims to facilitate advancements in user identification methods and promote reproducibility in XR research. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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