Application of Artificial Intelligence in College Students' Physical Education and Competition

Abstract

With the advancement of the new educational curriculum reform, school physical education faces new challenges. Improving the efficiency and quality of school training has become a key research focus. Using the Openpose pose estimation algorithm, this paper obtained skeletal position data and analyzed differential pose features based on skeletal geometry. The proposed pose matching algorithm enabled action pose matching and evaluation of action standard degree, providing scores. By identifying deviations in non-standard movements, it guides students in learning basic physical movements and offers methods for assessing these movements in physical education. Data were analyzed with SPSS using an independent sample T-test. Results showed that the deviation between video heart rate under fluorescent lighting and wristband heart rate in a normal environment was around 3%. Post-exercise heartbeat measured by video differed by approximately 4.3% from the actual heartbeat. Significant differences were found in fostering students' learning motivation (T=-4.158), learning experience (T=-2.502), and learning outcomes (T=-8.617).