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Validation of deep learning-based markerless 3D pose estimation

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  • Veronika Kosourikhina
  • Diarmuid Kavanagh
  • Michael J Richardson
  • David M Kaplan

Abstract

Deep learning-based approaches to markerless 3D pose estimation are being adopted by researchers in psychology and neuroscience at an unprecedented rate. Yet many of these tools remain unvalidated. Here, we report on the validation of one increasingly popular tool (DeepLabCut) against simultaneous measurements obtained from a reference measurement system (Fastrak) with well-known performance characteristics. Our results confirm close (mm range) agreement between the two, indicating that under specific circumstances deep learning-based approaches can match more traditional motion tracking methods. Although more work needs to be done to determine their specific performance characteristics and limitations, this study should help build confidence within the research community using these new tools.

Suggested Citation

  • Veronika Kosourikhina & Diarmuid Kavanagh & Michael J Richardson & David M Kaplan, 2022. "Validation of deep learning-based markerless 3D pose estimation," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-11, October.
    • Handle: RePEc:plo:pone00:0276258
    DOI: 10.1371/journal.pone.0276258
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    References listed on IDEAS

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    1. Daniel Linke & Daniel Link & Martin Lames, 2018. "Validation of electronic performance and tracking systems EPTS under field conditions," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-19, July.
    2. Monya Baker, 2016. "1,500 scientists lift the lid on reproducibility," Nature, Nature, vol. 533(7604), pages 452-454, May.
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