Purpose

The main goal of the physical education (PE) environment is that each individual trained should achieve self-fulfillment with the large group of students involved with their own efforts. Deep learning is applying transferrable knowledge in new situations to help the students master in tough circumstances. In PE training, injuries occur when working together as a team. Safety measures are taken immediately as an emergency response to reduce the potential risk in students by providing first aid. To provide safety measures for the injured student immediately, the environment is monitored in real-time using a GPS.

Design/methodology/approach

Theory of Humanities Education (ToHE) infers that it has less collection of theories and a wide range of applications than the state-of-the-art systems. ToHE allows students to think creatively and play a vital role in one’s health which is a critical aspect in PE. The ToHE theory focuses on two main concepts, i.e. by using a methodological approach to analyse and deep learning to solve the problem. PE motivates college students to follow a healthy and active lifestyle.

Findings

The proposed system is deployed in real time for monitoring the student’s performance and provides an emergency response with an accuracy rate of 90%.

Originality/value

The deep learning offers solutions to the injuries by using the deep convolutional neural network to provide interpretability of the consequence by training it with various injuries that occur in the playground and inappropriate use of sports equipment. A case study provided in this paper outlines an emergency response scenario to an injured student in sports training.

中文翻译：

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使用深度学习在大学体育训练期间对抗伤害的应急响应系统

目的

体育 (PE) 环境的主要目标是，每个受过训练的人都应该通过自己的努力与大量学生一起实现自我实现。深度学习是在新情况下应用可转移的知识，帮助学生在艰难的情况下掌握。在体育训练中，作为一个团队一起工作时会发生伤害。立即采取安全措施作为应急响应，通过提供急救来降低学生的潜在风险。为了立即为受伤学生提供安全措施，使用 GPS 实时监控环境。

设计/方法/方法

人文教育理论（ToHE）推断，与最先进的系统相比，它的理论集合更少，应用范围更广。ToHE 允许学生创造性地思考并在个人健康方面发挥重要作用，这是体育的一个关键方面。ToHE 理论侧重于两个主要概念，即使用方法论方法进行分析和深度学习来解决问题。体育激励大学生遵循健康积极的生活方式。

发现

所提议的系统实时部署，用于监控学生的表现，并以 90% 的准确率提供紧急响应。

原创性/价值

深度学习通过使用深度卷积神经网络提供对伤害的解决方案，通过在操场上发生的各种伤害和运动器材的不当使用对其进行训练，从而提供对后果的可解释性。本文提供的案例研究概述了体育训练中受伤学生的应急响应场景。