Design and Application of a Student Physical Health Monitoring and Management System

The physical and health status of students forms the foundation of a nation’s growth and serves as a key measure of its overall strength. At present, enhancing the physical wellbeing of university students in India is at a crucial stage, facing several complex challenges. Educational institutions often overlook the importance of students’ physical health and lack a systematic and scientific approach to monitor and manage it. As a result, the current assessment methods are not highly effective, making it difficult to detect and address health issues promptly. This leads to significant barriers in the effective management of student health. To monitor the student’s physical health monitoring and management system, this paper proposes a novel intelligent framework called Modified Gated Units (MGU) based student physical health monitoring (SPHM) to monitor and classify the student’s fitness health. This framework aids in the better monitoring and managing the student physical health conditions. The comprehensive experimentation is carried out using Kaggle Student Health Monitoring Datasets, thereby measuring performance metrics such as accuracy, precision, recall and F1-score respectively. The experimental results indicate that the proposed framework achieved an accuracy of 99.3%, with a precision of 99.1%, recall of 98.4%, and an F1-score of 98.4%, surpassing the performance of other state-of-the-art models. This research opens the bright path of using artificial intelligence (AI) in SPHM for better analysing the students’ physical health and supports for the growth of any nation.