A Spatial-Temporal Transformer based Framework For Human Pose Assessment
  And Correction in Education Scenarios
Wenyang Hu Kai Liu Libin Liu Huiliang Shang
Human pose assessment and correction play a crucial role in applications
across various fields, including computer vision, robotics, sports analysis,
healthcare, and entertainment. In this paper, we propose a Spatial-Temporal
Transformer based Framework (STTF) for human pose assessment and correction in
education scenarios such as physical exercises and science experiment. The
framework comprising skeletal tracking, pose estimation, posture assessment,
and posture correction modules to educate students with professional,
quick-to-fix feedback. We also create a pose correction method to provide
corrective feedback in the form of visual aids. We test the framework with our
own dataset. It comprises (a) new recordings of five exercises, (b) existing
recordings found on the internet of the same exercises, and (c) corrective
feedback on the recordings by professional athletes and teachers. Results show
that our model can effectively measure and comment on the quality of students'
actions. The STTF leverages the power of transformer models to capture spatial
and temporal dependencies in human poses, enabling accurate assessment and
effective correction of students' movements.