{"title":"Stress detection with encoding physiological signals and convolutional neural network","authors":"Michela Quadrini, Antonino Capuccio, Denise Falcone, Sebastian Daberdaku, Alessandro Blanda, Luca Bellanova, Gianluca Gerard","doi":"10.1007/s10994-023-06509-4","DOIUrl":null,"url":null,"abstract":"<p>Stress is a significant and growing phenomenon in the modern world that leads to numerous health problems. Robust and non-invasive method developments for early and accurate stress detection are crucial in enhancing people’s quality of life. Previous researches show that using machine learning approaches on physiological signals is a reliable stress predictor by achieving significant results. However, it requires determining features by hand. Such a selection is a challenge in this context since stress determines nonspecific human responses. This work overcomes such limitations by considering STREDWES, an approach for Stress Detection from Wearable Sensors Data. STREDWES encodes signal fragments of physiological signals into images and classifies them by a Convolutional Neural Network (CNN). This study aims to study several encoding methods, including the Gramian Angular Summation/Difference Field method and Markov Transition Field, to evaluate the best way to encode signals into images in this domain. Such a study is performed on the NEURO dataset. Moreover, we investigate the usefulness of STREDWES in real scenarios by considering the SWELL dataset and a personalized approach. Finally, we compare the proposed approach with its competitors by considering the WESAD dataset. It outperforms the others.</p>","PeriodicalId":49900,"journal":{"name":"Machine Learning","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine Learning","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10994-023-06509-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Stress is a significant and growing phenomenon in the modern world that leads to numerous health problems. Robust and non-invasive method developments for early and accurate stress detection are crucial in enhancing people’s quality of life. Previous researches show that using machine learning approaches on physiological signals is a reliable stress predictor by achieving significant results. However, it requires determining features by hand. Such a selection is a challenge in this context since stress determines nonspecific human responses. This work overcomes such limitations by considering STREDWES, an approach for Stress Detection from Wearable Sensors Data. STREDWES encodes signal fragments of physiological signals into images and classifies them by a Convolutional Neural Network (CNN). This study aims to study several encoding methods, including the Gramian Angular Summation/Difference Field method and Markov Transition Field, to evaluate the best way to encode signals into images in this domain. Such a study is performed on the NEURO dataset. Moreover, we investigate the usefulness of STREDWES in real scenarios by considering the SWELL dataset and a personalized approach. Finally, we compare the proposed approach with its competitors by considering the WESAD dataset. It outperforms the others.
期刊介绍:
Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.