CITATION.cff

cff-version: 1.2.0
title: >-
  Who is Alyx? – A Virtual Reality Motion and Eye-Tracking
  Multi-Session Dataset
message: >-
  If you use this dataset, please cite both the article from
  preferred-citation and the dataset itself.
type: dataset
authors:
  - family-names: Rack
    orcid: "https://orcid.org/0000-0002-0022-0711"
    given-names: Christian
  - family-names: Sieper
    given-names: Fabian
  - family-names: Schach
    given-names: Lukas
  - family-names: Latoschik
    given-names: Marc Erich
doi: 10.5281/zenodo.8379914
repository-code: "https://github.com/cschell/who-is-alyx"
abstract: >-
  This dataset contains over 110 hours of motion,
  eye-tracking and physiological data from 71 players of the
  virtual reality game “Half-Life: Alyx”. Each player played
  the game on two separate days for about 45 minutes using a
  HTC Vive Pro.
keywords:
  - dataset
  - motion data
  - virtual reality
  - eye tracking
  - physiological data
license: CC-BY-NC-SA-4.0
date-released: "2022-04-21"
preferred-citation:
  type: article
  authors:
    - family-names: Rack
      orcid: "https://orcid.org/0000-0002-0022-0711"
      given-names: Christian
    - family-names: Fernando
      given-names: Tamara
    - family-names: Yalcin
      given-names: Murat
    - family-names: Hotho
      given-names: Andreas
    - family-names: Latoschik
      given-names: Marc Erich
  title: >-
    Who is Alyx? A new behavioral biometric dataset for user identification in
    XR
  journal: Frontiers in Virtual Reality
  volume: 4
  year: 2023
  url: "https://www.frontiersin.org/articles/10.3389/frvir.2023.1272234"
  doi: 10.3389/frvir.2023.1272234
  abstract: >-
    Introduction: This paper addresses the need for reliable user identification
    in Extended Reality (XR), focusing on the scarcity of public datasets in
    this area. Methods: We present a new dataset collected from 71 users who
    played the game 'Half-Life: Alyx' on an HTC Vive Pro for 45 min across two
    separate sessions. The dataset includes motion and eye-tracking data, along
    with physiological data from a subset of 31 users. Benchmark performance is
    established using two state-of-the-art deep learning architectures,
    Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU).
    Results: The best model achieved a mean accuracy of 95% for user
    identification within 2 min when trained on the first session and tested on
    the second. Discussion: The dataset is freely available and serves as a
    resource for future research in XR user identification, thereby addressing a
    significant gap in the field. Its release aims to facilitate advancements in
    user identification methods and promote reproducibility in XR research.
  license: CC-BY-4.0
  date-released: "2023-11-10"