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@INPROCEEDINGS{Schmitz:285366,
      author       = {Schmitz, Lio and Plack, Markus and Koyak, Berkan and Ullah,
                      Ehsan and Aziz, Ahmad and Klein, Reinhard and Lähner, Zorah
                      and Dröge, Hannah},
      title        = {{T}owards {A}utomated {A}nalysis of {G}aze {B}ehavior from
                      {C}onsumer {VR} {D}evices for {N}eurological {D}iagnosis},
      publisher    = {WORLD SCIENTIFIC},
      reportid     = {DZNE-2026-00230},
      pages        = {219-235},
      year         = {2025},
      note         = {Missing Journal: Pac Symp Biocomput = 2335-6928 (import
                      from CrossRef Conference, PubMed, , Journals: pub.dzne.de)},
      comment      = {Biocomputing 2026 : [Proceedings] - WORLD SCIENTIFIC, 2025.
                      - ISBN 978-981-98-2474-8978-981-98-2475-5 -
                      $doi:10.1142/9789819824755_0016$},
      booktitle     = {Biocomputing 2026 : [Proceedings] -
                       WORLD SCIENTIFIC, 2025. - ISBN
                       978-981-98-2474-8978-981-98-2475-5 -
                       $doi:10.1142/9789819824755_0016$},
      abstract     = {Recent studies have demonstrated that eye tracking is a
                      valuable tool in the detection, classification and staging
                      of neurodegenerative diseases such as Parkinson's Disease
                      (PD). However, traditional methods for capturing gaze data
                      often rely on expensive and non-engaging clinical equipment
                      such as video-oculography, limiting their accessibility and
                      scalability. In this work, we investigate the feasibility of
                      using eye tracking data collected via consumer-grade virtual
                      reality (VR) headsets to support neurological diagnostics in
                      a more accessible and user-friendly manner.This approach
                      enables large-scale, low-cost, and remote assessments, which
                      are particularly valuable in early detection and monitoring
                      of neurodegenerative conditions. We show that relevant
                      oculomotor features extracted from VR-based eye tracking can
                      be used for predictive assessment. Despite the inherent
                      noise and lower precision of consumer devices, careful
                      preprocessing and robust feature engineering, including deep
                      learning embeddings, mitigate these limitations. Our results
                      demonstrate that both handcrafted and learned features from
                      gaze behavior enable promising levels of classification
                      performance. This research represents an important step
                      towards scalable, automated, and accessible diagnostic tools
                      for neurodegenerative diseases using ubiquitous VR
                      technology.},
      month         = {Jan},
      date          = {2026-01-03},
      organization  = {Pacific Symposium on Biocomputing
                       2026, Kohala Coast (Hawaii), 3 Jan 2026
                       - 7 Jan 2026},
      cin          = {AG Aziz},
      cid          = {I:(DE-2719)5000071},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1142/9789819824755_0016},
      url          = {https://pub.dzne.de/record/285366},
}