The Full-scale Assembly Simulation Testbed (FAST) Dataset

Alec G. Moore, Tiffany D. Do, Nayan N. Chawla, Antonia Jimenez Iriarte, Ryan P. McMahan
3/13/2024
cs.HCcs.LG

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Abstract

In recent years, numerous researchers have begun investigating how virtual reality (VR) tracking and interaction data can be used for a variety of machine learning purposes, including user identification, predicting cybersickness, and estimating learning gains. One constraint for this research area is the dearth of open datasets. In this paper, we present a new open dataset captured with our VR-based Full-scale Assembly Simulation Testbed (FAST). This dataset consists of data collected from 108 participants (50 females, 56 males, 2 non-binary) learning how to assemble two distinct full-scale structures in VR. In addition to explaining how the dataset was collected and describing the data included, we discuss how the dataset may be used by future researchers.

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Cite this paper

@article{moore2024fullscale,
  title  = {The Full-scale Assembly Simulation Testbed (FAST) Dataset},
  author = {Alec G. Moore and Tiffany D. Do and Nayan N. Chawla and Antonia Jimenez Iriarte and Ryan P. McMahan},
  year   = {2024},
  eprint = {2403.08969},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.08969v2}
}

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