Datasets
Standard Dataset
Training and gaming 6DoF XR mobility
- Citation Author(s):
- Submitted by:
- Sam De Kunst
- Last updated:
- Wed, 11/29/2023 - 03:25
- DOI:
- 10.5281/zenodo.8224633
- Data Format:
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Abstract
Extended reality (XR) head-mounted displays (HMDs) are increasingly starting to rely on wireless task
offloading in a bid to allow unobstructed XR user movement, while still rendering high-resolution video on
a remote processing node. An example is the Oculus (Meta) Quest 2. However, congestion and reliability
issues associated with the wireless network can cause high latency and an overall low quality of service (QoS).
Therefore, understanding XR user mobility is of vital importance for supporting XR applications in future
wireless networks.
2 Research question
XR user mobility varies between applications and can be classified on the basis thereof. The question is,
how pronounced is user mobility while immersed in different applications? The study aims to generate a
detailed database of XR user mobility for inclusion in wireless network research and establish similarities in
the observed mobility, based on XR application type. For example, classify user mobility w.r.t. head angular
velocity, hand position, and user movement speed.
Each recording has multiple files, starting with a \gls{FBX} file that contains a 3D animation of the recording. A viewer like Autodesk FBX preview (cite link) can be used to render the preview in a 3D environment and replay it. The .fbx file can also be used to import the tracking data into tools like MatLab, but the data was resampled and isn't as accurate as the original raw data, so we suggest to only use it for previewing recordings.