A wearable-based sports health monitoring system using CNN and LSTM with self-attentions

Abstract

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.

Infodemiology of Influenza-like Illness: Utilizing Google Trends’ Big Data for Epidemic Surveillance

“…LSTM has been widely used to predict events with time series and can be successfully applied. It also outperforms most non-parametric methods [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86]. Related research, including flow forecasts, climate forecasts, and health monitoring, are shown in Table 2.…”