Intelligent long jump evaluation system integrating blazepose human pose assessment algorithm in higher education sports teaching - Tao Wang (Systems and Soft Computing)
There are issues in current higher education long jump teaching, e.g., assessment relies on teachers' experience, lacks scientific evaluation, and can't quantitatively give performance feedback to students. To address these issues, this research first divides the long jump process into the approach run and mid-air phases. Secondly, it proposes a method for measuring approach run speed based on virtual line velocity algorithm. Subsequently, by combining the BlazePose human pose assessment algorithm with posture matching algorithms, a technique for assessing mid-air long jump movements integrated with BlazePose human pose assessment algorithm is designed. Finally, an intelligent long jump evaluation system incorporating the BlazePose human pose assessment algorithm is established. The research findings demonstrate that the average accuracy of video at 120FPS reaches a maximum of 94.47%. The assessment accuracy of mid-air long jump movements integrated with the BlazePose human pose assessment algorithm is highest, with accuracies of 94%, 90%, and 88% for the takeoff, hip extension, and abdominal contraction key movements respectively. Additionally, the method shows a scoring result with an average error range of 3 points compared to evaluations by professional teachers. In the practical application of the BlazePose human pose assessment algorithm's intelligent long jump evaluation system, evaluation scores and long jump proficiency receive scientifically objective assessments, while teachers provide targeted corrective feedback, achieving good application results. In summary, the proposed intelligent long jump evaluation system exhibits good performance, complete functionality, and can provide quantifiable data references for both teachers and students.