The Application System of Intelligent Wearable Devices in Physical Education

Abstract

To address the challenge of real-time data collection and analysis in human motion data mining, the author proposes a system that integrates intelligent wearable devices into physical education. Initially, motion data is gathered through these smart devices, then transformed into binary format. This data undergoes cleaning and supplementation processes before being clustered. The Firefly Algorithm is employed to enhance the K-means clustering technique, which is then applied to the processed data. Experimental results indicate that this refined approach achieves an average recall rate of 97.12% and an average data mining accuracy of 98.42%, thus offering a valuable foundation for the real-time monitoring and assessment of students’ physiological metrics. This algorithm can be applied in student physical condition assessment, sports injury and fatigue monitoring, sports posture and movement assessment, personalized training and rehabilitation program development, scientific decision-making and management, and other aspects.