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Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio

Author

Listed:
  • Praneet C. Bala

    (University of Minnesota)

  • Benjamin R. Eisenreich

    (University of Minnesota)

  • Seng Bum Michael Yoo

    (University of Minnesota)

  • Benjamin Y. Hayden

    (University of Minnesota
    University of Minnesota
    University of Minnesota)

  • Hyun Soo Park

    (University of Minnesota)

  • Jan Zimmermann

    (University of Minnesota
    University of Minnesota
    University of Minnesota)

Abstract

The rhesus macaque is an important model species in several branches of science, including neuroscience, psychology, ethology, and medicine. The utility of the macaque model would be greatly enhanced by the ability to precisely measure behavior in freely moving conditions. Existing approaches do not provide sufficient tracking. Here, we describe OpenMonkeyStudio, a deep learning-based markerless motion capture system for estimating 3D pose in freely moving macaques in large unconstrained environments. Our system makes use of 62 machine vision cameras that encircle an open 2.45 m × 2.45 m × 2.75 m enclosure. The resulting multiview image streams allow for data augmentation via 3D-reconstruction of annotated images to train a robust view-invariant deep neural network. This view invariance represents an important advance over previous markerless 2D tracking approaches, and allows fully automatic pose inference on unconstrained natural motion. We show that OpenMonkeyStudio can be used to accurately recognize actions and track social interactions.

Suggested Citation

  • Praneet C. Bala & Benjamin R. Eisenreich & Seng Bum Michael Yoo & Benjamin Y. Hayden & Hyun Soo Park & Jan Zimmermann, 2020. "Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18441-5
    DOI: 10.1038/s41467-020-18441-5
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    Cited by:

    1. Ana M. G. Manea & David J.-N. Maisson & Benjamin Voloh & Anna Zilverstand & Benjamin Hayden & Jan Zimmermann, 2024. "Neural timescales reflect behavioral demands in freely moving rhesus macaques," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Liang An & Jilong Ren & Tao Yu & Tang Hai & Yichang Jia & Yebin Liu, 2023. "Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Daniel J. Butler & Alexander P. Keim & Shantanu Ray & Eiman Azim, 2023. "Large-scale capture of hidden fluorescent labels for training generalizable markerless motion capture models," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Shaokai Ye & Anastasiia Filippova & Jessy Lauer & Steffen Schneider & Maxime Vidal & Tian Qiu & Alexander Mathis & Mackenzie Weygandt Mathis, 2024. "SuperAnimal pretrained pose estimation models for behavioral analysis," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

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