Abstract
In the field of education, sports in universities are an indispensable component, and the associated sports risks are widely concerned by society. This is because in previous university sports, insufficient technological support and lack of monitoring of vital health signs resulted in ineffective results, and even cases of sudden sports death occurred. Therefore, this article proposes the use of optical imaging technology and big data algorithms to design a sports and health monitoring system for universities. Firstly, by utilizing the characteristics of the imaging system for computing light fields, the imaging technology for computing light fields is combined with adaptive optical imaging. Based on the distribution of information in the light field, the internal four-dimensional light field information is distinguished and filtered. Secondly, after fully considering the issues of model design and data processing, this article studies the application of distributed algorithms in large-scale data scenarios, and then uses the MapReduce model to handle the problems of distributed big data algorithms, which significantly improves the speed of intelligent analysis and calculation. In the end, this article designed a health monitoring system for college sports, which dynamically monitors physiological parameters such as blood pressure, blood oxygen, heart rate, and body temperature of students. The results show that this method can effectively warn of sports risks such as fatigue and sudden sports death, providing protection for the health training of college sports majors.