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

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.

Functional movement screen dataset collected with two Azure Kinect depth sensors.

This paper presents a dataset for vision-based autonomous Functional Movement Screen (FMS) collected from 45 human subjects of different ages (18-59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, quaternions, 3D human skeleton joints and 2D pixel trajectories of 32 joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 190 GB. This dataset provides the opportunity for automatic action quality evaluation of FMS.