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

Yuan-Yuan Shen; Qing-Jun Xing; Yan-Fei Shen

doi:10.1016/j.isci.2023.108705

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

iScience. 2023 Dec 14;27(1):108705. doi: 10.1016/j.isci.2023.108705. eCollection 2024 Jan 19.

Authors

Yuan-Yuan Shen¹, Qing-Jun Xing², Yan-Fei Shen¹

Affiliations

¹ School of Sport Engineering, Beijing Sport University, Beijing 100084, China.
² School of Sport Science, Beijing Sport University, Beijing 100084, China.

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 demonstrate that our MVDNN outperforms current state-of-the-art methods with an average miF1 score of 0.857, maF1 score of 0.768, and Kappa score of 0.640 over ten runs.

Keywords: Artificial intelligence; Machine learning; Sports medicine.

Associated data

figshare/10.25452/figshare.plus.c.5774969.v1