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.

该论文提出了一种基于时空转换器的人体姿势评估和纠正框架 (STTF)，用于教育场景中的体育锻炼和科学实验，通过骨骼跟踪、姿势估计、姿势评估和姿势纠正模块为学生提供专业的及时反馈，并利用姿势纠正方法提供可视化辅助纠正反馈。研究结果表明，该模型能有效地评估和评价学生动作的质量，通过利用转换器模型捕捉人体姿势的时空依赖关系，实现了准确的评估和有效的纠正学生动作。