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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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CREATTIVE3D multimodal dataset of user behavior in virtual reality

Authors: Hui-Yin Wu; Florent Robert; Lucile Sassatelli; Marco Winckler; Auriane Gros; Stephen Ramanoël;

CREATTIVE3D multimodal dataset of user behavior in virtual reality

Abstract

In the context of the ANR CREATTIVE3D project, we join the expertise of computer science, neuroscience, and clinical practitioners, with the aim to analyze the impact that a simulated low-vision condition has on user navigation behavior in complex road crossing scenes: a common daily situation where the difficulty to access and process visual information (e.g., traffic lights, approaching cars) in a timely fashion can lead to serious consequences on a person's safety and well-being. As a secondary objective, we also aim to investigate the potential role virtual reality could play in rehabilitation and training protocols for low-vision patients. This dataset contains the data as part of the study described in An Integrated Framework for Understanding Multimodal Embodied Experiences in Interactive Virtual Reality. The dataset is metadata for the pre-print Exploring, walking, and interacting in virtual reality with simulated low vision: a living contextual dataset To use this dataset, please cite: @unpublished{wu:hal-04429351, TITLE = {{Exploring, walking, and interacting in virtual reality with simulated low vision: a living contextual dataset}}, AUTHOR = {Wu, Hui-Yin and Robert, Florent Alain Sauveur and Gallo, Franz Franco and Pirkovets, Kateryna and Quere, Cl{\'e}ment and Delachambre, Johanna and Ramano{\"e}l, Stephen and Gros, Auriane and Winckler, Marco and Sassatelli, Lucile and Hayotte, Meggy and Menin, Aline and Kornprobst, Pierre}, URL = {https://inria.hal.science/hal-04429351}, NOTE = {working paper or preprint}, YEAR = {2023}, MONTH = Dec, KEYWORDS = {Virtual reality ; Dataset ; Context ; Low vision ; 3D environments ; User study}, PDF = {https://inria.hal.science/hal-04429351/file/2023_CREATTIVE3D_dataset_arxiv_.pdf}, HAL_ID = {hal-04429351}, HAL_VERSION = {v1}, }

Keywords

physiology, motion capture, virtual reality, road crossing, low vision, gaze tracking

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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