XR training and gameplay 6DoF mobility dataset

• Main Authors: De Kunst, Sam, Marinšek Alexander, Callebaut, Gilles, De Strycker, Lieven, Van der Perre, Liesbet
• Format: Other/Unknown Material
• Language: unknown
• Published: Zenodo 2024
• Subjects: virtual reality; extended reality; vive; tundra; body tracking; steamvr; openvr; vr; xr; mobility; tracking data; head-mounted display; Tundra
• Online Access: https://doi.org/10.5281/zenodo.10836884

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)