Markerless vision-based functional movement screening movements evaluation with deep neural networks.

ABSTRACT: The functional movement screen (FMS) test is a seven-test battery used to assess fundamental movement abilities of individuals. It is commonly used to predict sports injuries but relies on clinical expertise and is not suitable for self-examination. This study presents an automatic FMS movement assessment framework using a multi-view deep neural network called MVDNN. The framework combines automatic skeleton extraction with manual feature selection to extract 3D trajectory features of human skeleton joints from two different directions. Three mainstream methods of time-series modeling are then used to learn high-level feature representation from skeleton sequences, and motion features from two views are fused to provide complementary information. Results of public FMS movements dataset dem

SUBMITTER: Shen YY 

PROVIDER: S-EPMC10784700 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature