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A wearable-based sports health monitoring system using CNN and LSTM with self-attentions.
- Source :
-
PLoS ONE . 10/11/2023, Vol. 18 Issue 10, p1-14. 14p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 18
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 172917654
- Full Text :
- https://doi.org/10.1371/journal.pone.0292012