Assessment of monocular human pose estimation models for clinical movement analysis.

ABSTRACT: Recent advances in computer vision and learning-based approaches have led to the emergence of markerless human pose estimation as a promising alternative to established motion capturing methods. Markerless methods offer several advantages, including low hardware requirements, affordability, fast setup, and reduced post-processing efforts. However, the accuracy of these methods and the speed at which poses are inferred tend to be lower when compared with marker-based optical motion capturing, which might restrict their range of applications. This study assessed the accuracy, precision, and inference speed of 11 different open source monocular markerless human pose estimators. For this we created Physio2.2M, a dataset comprising 2.2 million RGB frames of 25 unimpaired participants engaged in

SUBMITTER: Rode D 

PROVIDER: S-EPMC12589393 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature