Sustainable Detection and Monitoring of Psychiatric Risk and Abnormal Behavioral States using Multimodal Biosensor Data

Sustainable Detection and Monitoring of Psychiatric Risk and Abnormal Behavioral States using Multimodal Biosensor 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 Research Article Sustainable Detection and Monitoring of Psychiatric Risk and Abnormal Behavioral States using Multimodal Biosensor Data Akshaya D. Shetty, Usha Desai, Akash Saxena This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7807886/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 Individuals exhibiting neurological, developmental and behavioral disorders can manifest stress, agitation and emotional deregulation. The prolonged emotional deregulation increases the development of depression, anxiety disorders and in extreme cases, this persistent deregulation contributes to onset of psychiatric symptoms. This study proposes a novel approach for the detection of psychiatric risk and abnormal behavioral states using multimodal biosensors integration namely Pulse, Electromyography (EMG), and Galvanic Skin Response (GSR) sensors. This experiment conducted in a skill-training centre for Endosulfan victims of age 13 ± 2 years with necessary care. Significant differences in the electro dermal signal such as GSR, pulse, EMG observed among the 66 subjects with multiple trails taken in this research work. Each subject data has multiple values of bio-signals. Data acquisition performed by integrating sensors onto the Arduino UNO microcontroller and Cool-Term software tool. In total, fifteen nonlinear transform domain features extracted. The confidence intervals of features verified using the t -test ( p < 0.05). Classification model created using the machine learning algorithms viz. Logistic Regression, Random Forests, Gradient Boosting, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), XGBoost, and Neural Network. In which, Random Forest, XGBoost, and Neural Network achieved a sustainable accuracy of 97.92%. This approach of biosensor monitoring helps to predict consistently a behavioral state of differently abled individuals and supports health care providers in early identifications of behavioral and physiological markers indicative of psychiatric risks and sustainable mental health ecosystem. Electrodermal Activity Skin Conductance Galvanic Skin Response (GSR) Biofeedback Nonlinear Signals 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7807886","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545666523,"identity":"71fba4c2-fd1b-4f17-ae6a-e479da2a98df","order_by":0,"name":"Akshaya D. 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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":"Electrodermal Activity, Skin Conductance, Galvanic Skin Response (GSR), Biofeedback, Nonlinear Signals","lastPublishedDoi":"10.21203/rs.3.rs-7807886/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7807886/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndividuals exhibiting neurological, developmental and behavioral disorders can manifest stress, agitation and emotional deregulation. The prolonged emotional deregulation increases the development of depression, anxiety disorders and in extreme cases, this persistent deregulation contributes to onset of psychiatric symptoms. This study proposes a novel approach for the detection of psychiatric risk and abnormal behavioral states using multimodal biosensors integration namely Pulse, Electromyography (EMG), and Galvanic Skin Response (GSR) sensors. This experiment conducted in a skill-training centre for Endosulfan victims of age 13\u0026thinsp;\u0026plusmn;\u0026thinsp;2 years with necessary care. Significant differences in the electro dermal signal such as GSR, pulse, EMG observed among the 66 subjects with multiple trails taken in this research work. Each subject data has multiple values of bio-signals. Data acquisition performed by integrating sensors onto the Arduino UNO microcontroller and Cool-Term software tool. In total, fifteen nonlinear transform domain features extracted. The confidence intervals of features verified using the \u003cem\u003et\u003c/em\u003e-test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Classification model created using the machine learning algorithms viz. Logistic Regression, Random Forests, Gradient Boosting, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), XGBoost, and Neural Network. In which, Random Forest, XGBoost, and Neural Network achieved a sustainable accuracy of 97.92%. This approach of biosensor monitoring helps to predict consistently a behavioral state of differently abled individuals and supports health care providers in early identifications of behavioral and physiological markers indicative of psychiatric risks and sustainable mental health ecosystem.\u003c/p\u003e","manuscriptTitle":"Sustainable Detection and Monitoring of Psychiatric Risk and Abnormal Behavioral States using Multimodal Biosensor Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 07:45:26","doi":"10.21203/rs.3.rs-7807886/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"a8a4aa2e-7c4a-46f4-aef2-3df170110e74","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T12:24:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 07:45:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7807886","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7807886","identity":"rs-7807886","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}