A skeleton-based method and benchmark for real-time action classification of functional movement screen

FMS stands for functional movement screen, which is a simple and effective method to evaluate athletes' basic sports ability. This paper proposed a real-time FMS action classification method. Correspondingly, a video set was constructed with two different perspectives including 8 testers, 13 independent testing processes and 360574 images. Moreover, a normalization algorithm and a result correction algorithm is proposed to improve the performance of the models and make the result sequence more continuously. Furthermore, it has the vital significance to FMS action evaluation. Finally, this paper analyzed the effectiveness of different models and compared their performance from the aspects of accuracy, continuity, running speed, etc. The experimental results show that our method can achieve 96.7% precision score and 94.7% recall score on the test set. And the average running speed of the system is over 30 FPS. All related data, benchmarks and codes will be uploaded on https://github.com/bobogo/FMS-evaluation-system.