The accuracy of several pose estimation methods for 3D joint centre localisation.

ABSTRACT: Human movement researchers are often restricted to laboratory environments and data capture techniques that are time and/or resource intensive. Markerless pose estimation algorithms show great potential to facilitate large scale movement studies 'in the wild', i.e., outside of the constraints imposed by marker-based motion capture. However, the accuracy of such algorithms has not yet been fully evaluated. We computed 3D joint centre locations using several pre-trained deep-learning based pose estimation methods (OpenPose, AlphaPose, DeepLabCut) and compared to marker-based motion capture. Participants performed walking, running and jumping activities while marker-based motion capture data and multi-camera high speed images (200 Hz) were captured. The pose estimation algorithms were applied

SUBMITTER: Needham L 

PROVIDER: S-EPMC8526586 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature