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A Spatial-Temporal Transformer based Framework For Human Pose Assessment And Correction in Education Scenarios

arXiv 2311.00401 ↗First posted 1 Nov 2023cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 1 Nov 2023.

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References · 18

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6 more
  1. 2019What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessmentcited by 100
  2. 2018Action Quality Assessment Across Multiple Actionscited by 78
  3. 2018The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videoscited by 68
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  5. 2022Lite Pose: Efficient Architecture Design for 2D Human Pose Estimationcited by 16
  6. 2017Human Pose Estimation using Global and Local Normalizationcited by 10

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