Affiliations: [a] College of Physical Education, Southwest Forestry University, Kunming, Yunnan, China
| [b] College of Physical Education, Pu’er University, Puer, Yunnan, China
| [c]
Library of Pu’er University, Puer, Yunnan, China
Correspondence:
[*]
Corresponding author. Shen Liu, College of Physical Education, Pu’er University, Puer, Yunnan, 665000, China. E-mail: liushen@mail-me.com.
Abstract: This article is based on artificial intelligence technology to recognize and identify risks in college sport. The application of motion recognition technology first need to collect the source data, store the collected data in the server database, collect the learner’s real-time data and return it to the database to achieve the purpose of real-time monitoring. It is found that in the identification of risk sources of sports courses, there are a total of 4 first-level risk factors, namely teacher factors, student factors, environmental factors, and school management factors, and a total of 15 second-level risk factors, which are teaching preparation, teaching process, and teaching effect. When the frequency of teaching risks is low, the consequence loss is small. When the frequency of teaching risks is low, the consequences are very serious. Risk mitigation is the main measure to reduce the occurrence of teaching risks and reduce the consequences of losses.
Keywords: Artificial intelligence, college sports, exercise risk, cognitive and
recognition applications
