The Integration of AR and Biometric Monitoring Technology in Physical Education: Practice and Business Opportunities

Jingjing Zhou
School of Physical Education, Shenzhen University, Shenzhen 518000, Guangdong, China
Ying Gao
School of Physical Education, Shenzhen University, Shenzhen 518000, Guangdong, China

Abstract:

Traditional physical education teaching lacks personalized feedback and data support, making it difficult to fully meet the diverse needs of students in terms of physical fitness and health management. Augmented reality technology is a cutting-edge information technology that integrates virtual information with the real world in real-time. It mainly enhances users' perception of the real environment by overlaying virtual content on the real environment they see through computer vision, image recognition, sensor positioning, and other means. Biological monitoring is also gradually extending from professional medical, military, and entertainment fields to the education field. Biological monitoring can provide real-time feedback on heart rate, muscle activity, electric skin reactions, and other data. AR systems can adjust training intensity in real time. The integration of AR and biological monitoring technology has strong application potential in the field of sports, and the immersive experience and personalized feedback system it brings endow great commercial development value. This study aims to achieve efficient personalized training through AR and biological monitoring technology, which can use MEMS inertial sensors to capture users' motion data. This article combines OpenPose and Mask R-CNN algorithms to effectively construct virtual scenes for AR rehabilitation training models, which effectively demonstrates the data acquisition of MEMS sensors using AR. Then, the system presented in this article can significantly improve the accuracy of students' exercise training by displaying different dimensions (such as wrist flexion and extension angles) in real-time. The research results show that the combination of AR and biological monitoring technology effectively improves the accuracy of sports training, especially in the training process, which can help students adjust their posture through real-time feedback to achieve higher training results. The accuracy of Mask R-CNN Valence is 0.7057, and the accuracy of Arousal is 0.6972. Furthermore, this study employed a grey prediction model to model and predict the commercial development trends of physical education. The results suggest that the market for physical education based on this technology holds broad commercial prospects, especially in the fields of personalized training and rehabilitation medicine. The innovation of this paper lies in its first-time integration of AR and biometric monitoring technology into physical education and the provision of a new interactive training experience through real-time motion data and virtual training feedback. This model breaks through the limitations of traditional physical education methods, offering a feasible technological path and business application value for the field. In the future, as technology continues to develop and costs decrease, the integration of AR and biometric monitoring technology will be widely applied in various fields, including physical education, health management, and rehabilitation therapy.