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Validating markerless pose estimation with 3D X-ray radiography.

ABSTRACT: To reveal the neurophysiological underpinnings of natural movement, neural recordings must be paired with accurate tracking of limbs and postures. Here, we evaluated the accuracy of DeepLabCut (DLC), a deep learning markerless motion capture approach, by comparing it with a 3D X-ray video radiography system that tracks markers placed under the skin (XROMM). We recorded behavioral data simultaneously with XROMM and RGB video as marmosets foraged and reconstructed 3D kinematics in a common coordinate system. We used the toolkit Anipose to filter and triangulate DLC trajectories of 11 markers on the forelimb and torso and found a low median error (0.228 cm) between the two modalities corresponding to 2.0% of the range of motion. For studies allowing this relatively small error, DLC and similar markerless pose estimation tools enable the study of increasingly naturalistic behaviors in many fields including non-human primate motor control.

SUBMITTER: Moore DD

PROVIDER: S-EPMC9163444 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Validating markerless pose estimation with 3D X-ray radiography.

Moore Dalton D DD Walker Jeffrey D JD MacLean Jason N JN Hatsopoulos Nicholas G NG

The Journal of experimental biology 20220512 9

To reveal the neurophysiological underpinnings of natural movement, neural recordings must be paired with accurate tracking of limbs and postures. Here, we evaluated the accuracy of DeepLabCut (DLC), a deep learning markerless motion capture approach, by comparing it with a 3D X-ray video radiography system that tracks markers placed under the skin (XROMM). We recorded behavioral data simultaneously with XROMM and RGB video as marmosets foraged and reconstructed 3D kinematics in a common coordin ...[more]

PMID: 35466360

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