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Validation of deep learning-based markerless 3D pose estimation.

Veronika Kosourikhina1, Diarmuid Kavanagh2,3, Michael J Richardson1,4

  • 1School of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.

Plos One
|October 20, 2022
PubMed
Summary
This summary is machine-generated.

Researchers validated DeepLabCut, a deep learning tool for markerless 3D pose estimation, against a reference system. Results show close agreement, suggesting deep learning can match traditional motion tracking methods.

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Area of Science:

  • Neuroscience
  • Psychology
  • Biomedical Engineering

Background:

  • Markerless 3D pose estimation tools using deep learning are rapidly adopted in psychology and neuroscience.
  • Many of these advanced tools lack rigorous validation against established methods.

Purpose of the Study:

  • To validate the performance of DeepLabCut, a popular deep learning-based markerless 3D pose estimation tool.
  • To compare DeepLabCut's accuracy against a reference measurement system (Fastrak) with known performance.

Main Methods:

  • Simultaneous measurements were collected using DeepLabCut and the Fastrak reference system.
  • Data from both systems were analyzed to assess agreement and accuracy.

Main Results:

  • DeepLabCut demonstrated close agreement, within millimeters, compared to the Fastrak system.
  • The findings indicate that deep learning approaches can achieve accuracy comparable to traditional motion tracking methods under certain conditions.

Conclusions:

  • Deep learning-based markerless 3D pose estimation tools like DeepLabCut show promise for neuroscience and psychology research.
  • Further research is needed to fully characterize the performance and limitations of these emerging technologies, but this validation builds confidence in their use.