Wang Tao Yuhuan, Cui Jiajia, Fan Yao
School of Physical Education, Northeast Normal University, Changchun Jilin, China.
Jilin Sport University, Changchun Jilin, China.
PLoS One. 2023 Oct 11;18(10):e0292012. doi: 10.1371/journal.pone.0292012. eCollection 2023.
Sports performance and health monitoring are essential for athletes to maintain peak performance and avoid potential injuries. In this paper, we propose a sports health monitoring system that utilizes wearable devices, cloud computing, and deep learning to monitor the health status of sports persons. The system consists of a wearable device that collects various physiological parameters and a cloud server that contains a deep learning model to predict the sportsperson's health status. The proposed model combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and self-attention mechanisms. The model is trained on a large dataset of sports persons' physiological data and achieves an accuracy of 93%, specificity of 94%, precision of 95%, and an F1 score of 92%. The sports person can access the cloud server using their mobile phone to receive a report of their health status, which can be used to monitor their performance and make any necessary adjustments to their training or competition schedule.
运动表现和健康监测对于运动员保持最佳状态并避免潜在受伤至关重要。在本文中，我们提出了一种运动健康监测系统，该系统利用可穿戴设备、云计算和深度学习来监测运动员的健康状况。该系统由一个收集各种生理参数的可穿戴设备和一个包含深度学习模型以预测运动员健康状况的云服务器组成。所提出的模型结合了卷积神经网络（CNN）、长短期记忆（LSTM）和自注意力机制。该模型在大量运动员生理数据的数据集上进行训练，准确率达到93%，特异性为94%，精确率为95%，F1分数为92%。运动员可以使用他们的手机访问云服务器以接收其健康状况报告，该报告可用于监测他们的表现，并对他们的训练或比赛日程进行任何必要的调整。
