Scalable Multi-Machine Imaging Techniques for Mental Health Enhancement in College Sports Programs

Abstract

The mental health of sports students and training are among the most challenging subjects for generalist teachers to include in their teaching confidently. The conventional mental health of sports students makes it hard to stimulate students’ interest in sports, leading to a poor participation rate and inability to exercise their bodies. This paper proposes a scalable multi-machine imaging Learning Framework (ILF) in the mental health of sports students and sports training to give students a new understanding of college mental health of sports students and sports training. It enhances college sportspersons’ technical level and training quality. This method delivers a generalist teacher via suitable professional development, a means for providing a high‐quality mental health program for sports students. It complements the repertoire of the specialist mental health of sports students and teachers at college and university levels. Experimental analysis has been taken on different sportsperson datasets based on the usage of digital technology, and its advancement in monitoring sports persons has been discussed suggestively in this study. The proposed ILF model increases the student activity analysis by 98.8% and the student physical workout level analysis by 97.5% compared to other existing models.