Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities

The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and biosensors benefitting from edge computing resources,...

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Published in:Scientific Reports
Main Author: Liu P.; Song Y.; Yang X.; Li D.; Khosravi M.
Format: Article
Language:English
Published: Nature Research 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198348349&doi=10.1038%2fs41598-024-66839-8&partnerID=40&md5=9a0c65bd19ee07017f5eb8852292d017
id 2-s2.0-85198348349
spelling 2-s2.0-85198348349
Liu P.; Song Y.; Yang X.; Li D.; Khosravi M.
Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities
2024
Scientific Reports
14
1
10.1038/s41598-024-66839-8
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198348349&doi=10.1038%2fs41598-024-66839-8&partnerID=40&md5=9a0c65bd19ee07017f5eb8852292d017
The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and biosensors benefitting from edge computing resources, we are now able to monitor the physiological fatigue of students participating in sports in real time. These devices can then be used to analyze the data using contemporary technology. In this paper, we present an innovative deep learning framework for forecasting fatigue in athletic students following physical exercise. It addresses the issue of lack of precision computational models and extensive data analysis in current approaches to monitoring students’ physical activity. In our study, we classified fatigue and non-fatigue based on photoplethysmography (PPG) signals. Several deep learning models are compared in the study. Using limited training data, determining the optimal parameters for PPG presents a significant challenge. For datasets containing many data points, several models were trained using PPG signals: a deep residual network convolutional neural network (ResNetCNN) ResNetCNN, an Xception architecture, a bidirectional long short-term memory (BILSTM), and a combination of these models. Training and testing datasets were assigned using a fivefold cross validation approach. Based on the testing dataset, the model demonstrated a proper classification accuracy of 91.8%. © The Author(s) 2024.
Nature Research
20452322
English
Article
All Open Access; Gold Open Access
author Liu P.; Song Y.; Yang X.; Li D.; Khosravi M.
spellingShingle Liu P.; Song Y.; Yang X.; Li D.; Khosravi M.
Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities
author_facet Liu P.; Song Y.; Yang X.; Li D.; Khosravi M.
author_sort Liu P.; Song Y.; Yang X.; Li D.; Khosravi M.
title Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities
title_short Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities
title_full Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities
title_fullStr Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities
title_full_unstemmed Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities
title_sort Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities
publishDate 2024
container_title Scientific Reports
container_volume 14
container_issue 1
doi_str_mv 10.1038/s41598-024-66839-8
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198348349&doi=10.1038%2fs41598-024-66839-8&partnerID=40&md5=9a0c65bd19ee07017f5eb8852292d017
description The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and biosensors benefitting from edge computing resources, we are now able to monitor the physiological fatigue of students participating in sports in real time. These devices can then be used to analyze the data using contemporary technology. In this paper, we present an innovative deep learning framework for forecasting fatigue in athletic students following physical exercise. It addresses the issue of lack of precision computational models and extensive data analysis in current approaches to monitoring students’ physical activity. In our study, we classified fatigue and non-fatigue based on photoplethysmography (PPG) signals. Several deep learning models are compared in the study. Using limited training data, determining the optimal parameters for PPG presents a significant challenge. For datasets containing many data points, several models were trained using PPG signals: a deep residual network convolutional neural network (ResNetCNN) ResNetCNN, an Xception architecture, a bidirectional long short-term memory (BILSTM), and a combination of these models. Training and testing datasets were assigned using a fivefold cross validation approach. Based on the testing dataset, the model demonstrated a proper classification accuracy of 91.8%. © The Author(s) 2024.
publisher Nature Research
issn 20452322
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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