Physical education and emergency response system using deep learning: A step toward sustainable development of physical education environment

Abstract

The physical education (PE) system’s key goal is to educate individuals and the large community of participating students to achieve self-fulfillment. Deep learning uses integrated expertise to help students master challenging conditions in unfamiliar contexts. It is normal to get injuries while training or playing, and as an emergency response to mitigating students’ future risks by the availability of first aid, safety steps are promptly taken. Therefore, this article suggests a “physical education and emergency response system using deep learning” (PSERS-DL) to handle such situations effectively. In real-time, the PE environment can be tracked using a global positioning system-enabled surveillance system to immediately provide the wounded student with protective measures. The acquired visuals are immediately analyzed using a deep learning model, convolutional neural network (CNN). The 27 layers proposed in the CNN model have been evaluated compared with other deep learning models. The simulation results showed that the proposed PSERS-DL can assure the emergency response with the highest accuracy of 97.61%. The experimental results showed that the proposed PSERS-DL model enhances an accuracy ratio of 95.6%, a performance ratio of 97.6%, movement detection analysis ratio of 96.3%, a learning rate of 95.2%, an efficiency ratio of 98.1%, a security ratio of 93.5%, a delay time ratio of 33.2%, and a behavior analysis ratio of 90.7% when compared to other existing approaches.