XR training and gameplay 6DoF mobility dataset ...
User mobility in extended reality (XR) can have a major impact on millimeter-wave (mmWave) links and may require dedicated mitigation strategies to ensure reliable connections and avoid service outages. The available prior art has predominantly focused on XR applications with constrained user mobili...
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Online Access: | https://dx.doi.org/10.5281/zenodo.8224632 https://zenodo.org/doi/10.5281/zenodo.8224632 |
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ftdatacite:10.5281/zenodo.8224632 2024-04-28T08:40:53+00:00 XR training and gameplay 6DoF mobility dataset ... De Kunst, Sam Marinšek Alexander Callebaut, Gilles De Strycker, Lieven Van der Perre, Liesbet 2024 https://dx.doi.org/10.5281/zenodo.8224632 https://zenodo.org/doi/10.5281/zenodo.8224632 unknown Zenodo https://dx.doi.org/10.5281/zenodo.10210058 https://dx.doi.org/10.5281/zenodo.10836884 https://dx.doi.org/10.5281/zenodo.8224633 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 virtual reality extended reality vive tundra body tracking steamvr openvr vr xr mobility tracking data head-mounted display dataset Dataset 2024 ftdatacite https://doi.org/10.5281/zenodo.822463210.5281/zenodo.1021005810.5281/zenodo.1083688410.5281/zenodo.8224633 2024-04-02T11:43:35Z User mobility in extended reality (XR) can have a major impact on millimeter-wave (mmWave) links and may require dedicated mitigation strategies to ensure reliable connections and avoid service outages. The available prior art has predominantly focused on XR applications with constrained user mobility and limited impact on mmWave channels. We have performed dedicated experiments to extend the characterisation of relevant future XR use cases featuring a high degree of user mobility. To this end, we have carried out a tailor-made XR mobility measurement campaign, capturing the movement of the head, hands, and body in 6DoF. For a usage example, see the provided Jupyter Notebook in cacerumd-usage-example.zip. A description of the measurement campaign and a characterisation of the recorded mobility can be found in the corresponding IEEE Magazine paper [coming soon] or a longer version, with more details about the experiment, on Arxiv [coming soon]. ... : Changelog: Added a Jupyter Notebook with usage examples Uploaded correct controller dataset (previously the latter consistet of a copy of the tracking data by mistake) Expanded volunteer XR experience information and added their handedness (incomplete since not all volunteers could be reached) ... Dataset Tundra DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
virtual reality extended reality vive tundra body tracking steamvr openvr vr xr mobility tracking data head-mounted display |
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virtual reality extended reality vive tundra body tracking steamvr openvr vr xr mobility tracking data head-mounted display De Kunst, Sam Marinšek Alexander Callebaut, Gilles De Strycker, Lieven Van der Perre, Liesbet XR training and gameplay 6DoF mobility dataset ... |
topic_facet |
virtual reality extended reality vive tundra body tracking steamvr openvr vr xr mobility tracking data head-mounted display |
description |
User mobility in extended reality (XR) can have a major impact on millimeter-wave (mmWave) links and may require dedicated mitigation strategies to ensure reliable connections and avoid service outages. The available prior art has predominantly focused on XR applications with constrained user mobility and limited impact on mmWave channels. We have performed dedicated experiments to extend the characterisation of relevant future XR use cases featuring a high degree of user mobility. To this end, we have carried out a tailor-made XR mobility measurement campaign, capturing the movement of the head, hands, and body in 6DoF. For a usage example, see the provided Jupyter Notebook in cacerumd-usage-example.zip. A description of the measurement campaign and a characterisation of the recorded mobility can be found in the corresponding IEEE Magazine paper [coming soon] or a longer version, with more details about the experiment, on Arxiv [coming soon]. ... : Changelog: Added a Jupyter Notebook with usage examples Uploaded correct controller dataset (previously the latter consistet of a copy of the tracking data by mistake) Expanded volunteer XR experience information and added their handedness (incomplete since not all volunteers could be reached) ... |
format |
Dataset |
author |
De Kunst, Sam Marinšek Alexander Callebaut, Gilles De Strycker, Lieven Van der Perre, Liesbet |
author_facet |
De Kunst, Sam Marinšek Alexander Callebaut, Gilles De Strycker, Lieven Van der Perre, Liesbet |
author_sort |
De Kunst, Sam |
title |
XR training and gameplay 6DoF mobility dataset ... |
title_short |
XR training and gameplay 6DoF mobility dataset ... |
title_full |
XR training and gameplay 6DoF mobility dataset ... |
title_fullStr |
XR training and gameplay 6DoF mobility dataset ... |
title_full_unstemmed |
XR training and gameplay 6DoF mobility dataset ... |
title_sort |
xr training and gameplay 6dof mobility dataset ... |
publisher |
Zenodo |
publishDate |
2024 |
url |
https://dx.doi.org/10.5281/zenodo.8224632 https://zenodo.org/doi/10.5281/zenodo.8224632 |
genre |
Tundra |
genre_facet |
Tundra |
op_relation |
https://dx.doi.org/10.5281/zenodo.10210058 https://dx.doi.org/10.5281/zenodo.10836884 https://dx.doi.org/10.5281/zenodo.8224633 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.5281/zenodo.822463210.5281/zenodo.1021005810.5281/zenodo.1083688410.5281/zenodo.8224633 |
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1797571369724018688 |