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The Full-scale Assembly Simulation Testbed (FAST) Dataset
Preprint   Open access

The Full-scale Assembly Simulation Testbed (FAST) Dataset

Alec G Moore, Tiffany D Do, Nayan N Chawla, Antonia Jimenez Iriarte and Ryan P McMahan
arXiv (Cornell University)
13 Mar 2024
DOI: https://doi.org/10.48550/arxiv.2403.08969
url
https://arxiv.org/abs/2403.08969View
Preprint (Author's original)arXiv.org - Non-exclusive license to distribute,  Open

Abstract

Computer Science - Human-Computer Interaction Computer Science - Learning
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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