A novel approach for yoga pose estimation based on in-depth analysis of human body joint detection accuracy

ABSTRACT: Virtual motion and pose from images and video can be estimated by detecting body joints and their interconnection. The human body has diverse and complicated poses in yoga, making its classification challenging. This study estimates yoga poses from the images using a neural network. Five different yoga poses, viz. downdog, tree, plank, warrior2, and goddess in the form of RGB images are used as the target inputs. The BlazePose model was used to localize the body joints of the yoga poses. It detected a maximum of 33 body joints, referred to as keypoints, covering almost all the body parts. Keypoints achieved from the model are considered as predicted joint locations. True keypoints, as the ground truth body joint for individual yoga poses, are identified manually using the open source image

SUBMITTER: Desai M 

PROVIDER: S-EPMC10280249 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature