From Signals to Emotions: A Machine LearningFramework for Robust Emotion Classification UsingMultimodal Physiological Data

From Signals to Emotions: A Machine LearningFramework for Robust Emotion Classification UsingMultimodal Physiological Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article From Signals to Emotions: A Machine LearningFramework for Robust Emotion Classification UsingMultimodal Physiological Data Debasish Tripathy, DAYANANDA PRUTHVIRAJA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7117245/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Emotion can be classified based on the physiological data and this is an important problem in the affective computing and it hasmany application in healthcare, human computer interaction and in the monitoring of mental health. A systematic comparativestudy of different machine learning models for emotion classification is proposed in this paper using a physiological signaldataset. The dataset consist of the following features heart rate (bpm), heart rate variability (hrv), QT interval, QRS duration,oxygen saturation (spo2), skin temperature, and anomaly indicators. We compare random forest, support vector machine (svm),xgboost, gradient boosting, lightgbm, catboost, adaboost with the neural network that is the ensemble of the most efficient. TheResults Show that The Highest AC has the Ensemble Model The data is presented in the Table 1 below. The accuracy of modelcomparison: The accuracy of the combination model is 93.97%, which is higher than that of other single models. The studyoffers contribution to the understanding of different machine learning approaches for Emotion categorization and the role ofensemble methods in improving the predictive accuracy. Furthermore, we are to discuss the impact of physiological anomalieson the efficiency of emotion categorization and its possible application in emotion monitoring systems. These aspects are ofgreat benefit to the field of affective computing and emotional health assessment. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7117245","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":491336441,"identity":"0d16de5f-ad5c-445d-856d-93e474ea090b","order_by":0,"name":"Debasish Tripathy","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Debasish","middleName":"","lastName":"Tripathy","suffix":""},{"id":491336442,"identity":"3c72a402-948d-4e5c-8eee-4c04e90b6894","order_by":1,"name":"DAYANANDA PRUTHVIRAJA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYDCCAxCKsQGIHwAZPAwMzEA2G3FamA0gWhiJ18ImgcTGrYPv+BnDzwU1d2T7G3iPVd2ouSNjzn6wgeFD2WGcWiTP5BhLzzj2zHjGAb602znHnvFY9iQ2MM44h1uLwYEcA2ketsOJDQd4zG7nsB3mMTiQ2MDM24ZHy/k3xr95/h1OnA/UUpzzD6jl/MMG5r/4tNzIMZMGmpm4AaiFObcNqOUG0BZGPFokbzwrs+btO2y88TBfsnRu32EeyxkPGw72nEvHqYXvfPLm2zzfDsvOO9578HPOt8P25vzJBx/8KLPGqYWBgcMAQjPzQJ3KAI8sXID9AZSBpGUUjIJRMApGATIAAAPGX/kAnmVLAAAAAElFTkSuQmCC","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"DAYANANDA","middleName":"","lastName":"PRUTHVIRAJA","suffix":""}],"badges":[],"createdAt":"2025-07-14 05:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7117245/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7117245/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88996025,"identity":"bcd1ace3-e30d-4caa-ad3b-9d5d3d5d367a","added_by":"auto","created_at":"2025-08-13 14:32:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1237187,"visible":true,"origin":"","legend":"","description":"","filename":"ngyxstspwpcjfmjnxmjhnkqxygbngpsm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7117245/v1_covered_0325259c-9ad1-4058-988f-babfe7a804ba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Signals to Emotions: A Machine LearningFramework for Robust Emotion Classification UsingMultimodal Physiological Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7117245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7117245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Emotion can be classified based on the physiological data and this is an important problem in the affective computing and it hasmany application in healthcare, human computer interaction and in the monitoring of mental health. A systematic comparativestudy of different machine learning models for emotion classification is proposed in this paper using a physiological signaldataset. The dataset consist of the following features heart rate (bpm), heart rate variability (hrv), QT interval, QRS duration,oxygen saturation (spo2), skin temperature, and anomaly indicators. We compare random forest, support vector machine (svm),xgboost, gradient boosting, lightgbm, catboost, adaboost with the neural network that is the ensemble of the most efficient. TheResults Show that The Highest AC has the Ensemble Model The data is presented in the Table 1 below. The accuracy of modelcomparison: The accuracy of the combination model is 93.97%, which is higher than that of other single models. The studyoffers contribution to the understanding of different machine learning approaches for Emotion categorization and the role ofensemble methods in improving the predictive accuracy. Furthermore, we are to discuss the impact of physiological anomalieson the efficiency of emotion categorization and its possible application in emotion monitoring systems. 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