Sports have become the important and most prominent play for each and every country to indulge their pride to the world. For this reason, countries are eager and interested in protecting the players/sportsmen in many ways such as health related, money, security etc. The life quality of sportsmen is improved by detecting huge potential known to be stress for preventing and managing the diseases. Moreover, low-cost wearable devices are available for monitoring the vital signs which leads to the detection of stress. Furthermore, the stress-levels are determined by using a particular vital sign known as Heart Rate Variability (HRV) that data is collected from a particular wearable device. In this paper, a real-time detection framework is proposed for analysing the level of stress for a particular sports person. The proposed framework consists of a hybrid classification technique named Multi-Output Regression (MOR) with Deep Convolutional Neural Networks (DCNN) to analyse and identify various stress levels and its relationship with data of HRV. Furthermore, 5-min time determination of each sportsman is distinguished based on their psychological and physical stress-levels. The simulation results show that the performance of the proposed framework obtains a high accuracy level when comparing with other models. With a lower error rate and based on efficiency, the proposed model achieves a high accuracy level of more than 96%.

体育已成为每个国家放纵自己对世界的骄傲的重要和最突出的运动。因此，各国渴望并有兴趣以多种方式保护运动员/运动员，例如健康，金钱，安全性等。通过发现巨大的潜力来预防和控制疾病，运动​​员的生活质量得到改善，从而提高了运动员的生活质量。此外，低成本的可穿戴设备可用于监视生命体征，从而检测压力。此外，可通过使用特定的生命体征（称为心率变异性（HRV））来确定压力水平，该生命体征是从特定的可穿戴设备收集的数据。本文提出了一种实时检测框架，用于分析特定运动者的压力水平。拟议的框架由称为多输出回归（MOR）和深度卷积神经网络（DCNN）的混合分类技术组成，用于分析和识别各种应力水平及其与HRV数据的关系。此外，根据每个运动员的心理和身体压力水平，可以确定5分钟的时间。仿真结果表明，与其他模型相比，该框架的性能达到了较高的准确度。提出的模型具有较低的错误率并基于效率，可以达到96％以上的高精度水平。根据每个运动员的心理和身体压力水平，分别确定5分钟的时间。仿真结果表明，与其他模型相比，该框架的性能达到了较高的准确度。提出的模型具有较低的错误率并基于效率，可以达到96％以上的高精度水平。根据每个运动员的心理和身体压力水平，分别确定5分钟的时间。仿真结果表明，与其他模型相比，该框架的性能达到了较高的准确度。提出的模型具有较低的错误率并基于效率，可以达到96％以上的高精度水平。