Validating marker-less pose estimation with 3D x-ray radiography.

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Abstract

To reveal the neurophysiological underpinnings of natural movement, neural recordings must be paired with accurate tracking of limbs and postures. Here we evaluate the accuracy of DeepLabCut (DLC), a deep learning marker-less motion capture approach, by comparing it to a 3D x-ray video radiography system that tracks markers placed under the skin (XROMM). We record behavioral data simultaneously with XROMM and RGB video as marmosets forage and reconstruct three-dimensional kinematics in a common coordinate system. We use Anipose to filter and triangulate DLC trajectories of 11 markers on the forelimb and torso and find 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 marker-less pose estimation tools enable the study of increasingly naturalistic behaviors in many fields including non-human primate motor control.© 2022. Published by The Company of Biologists Ltd.