Machine Learning-Based Intelligent Smart Embedded Sensors for Automatic Detection and Classification of Neuromuscular Disorders using EMG Signals

Machine Learning-Based Intelligent Smart Embedded Sensors for Automatic Detection and Classification of Neuromuscular Disorders using EMG Signals | 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 Machine Learning-Based Intelligent Smart Embedded Sensors for Automatic Detection and Classification of Neuromuscular Disorders using EMG Signals Abdelouahad Achmamad, Mohamed Elfezazi, Abdellah Chehri, Atman Jbari, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3865389/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 The objective of this work is to create a novel computer-aided health monitoring system for diagnosing neuromuscular disorders (NMDs). Additionally, we will propose the use of embedded sensor networks to facilitate proactive patient care and remote health monitoring. The proposed method combines the discrete wavelet transform (DWT) with two supervised machine learning algorithms: the multi-class support vector machine (SVM) and the k-nearest neighbors (k-NN) classifiers. The dataset includes ten normal subjects, aged between 21 and 37 years. Out of these subjects, six are males and four are females. The results were presented on a graphical user interface (GUI) based on LabVIEW and implemented using a real embedded CompactRIO-9035 real-time controller. Additionally, the proposed embedded system has the capability to serve as a portable diagnostic device for the automatic detection of NMDs. IoT-based Body Area Networks Instantaneous E-healthcare Services Classification CompactRIO Electromyography Signal Discrete Wavelet Transform Machine Learning 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-3865389","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267410789,"identity":"cfcd530c-9475-44fc-9d33-d9cfeb0e849e","order_by":0,"name":"Abdelouahad Achmamad","email":"","orcid":"","institution":"National Graduate School of Arts and Crafts ENSAM","correspondingAuthor":false,"prefix":"","firstName":"Abdelouahad","middleName":"","lastName":"Achmamad","suffix":""},{"id":267410790,"identity":"efadf471-2066-4eb1-ac7e-748210e6aca9","order_by":1,"name":"Mohamed Elfezazi","email":"","orcid":"","institution":"National Graduate School of Arts and Crafts ENSAM","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Elfezazi","suffix":""},{"id":267410791,"identity":"7396074a-416f-4bdd-98f2-7b7b11aa8638","order_by":2,"name":"Abdellah Chehri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnElEQVRIiWNgGAWjYDACCQY2BgYDG9K1pJGsheEwCTr4Zzcfe1xRcD5ft/0A44cfRFly51i64RmD25bbziQwS/YQo8VAIsdMssHgtoHZDaALeYjTkv8NqOUcWAvjHyJtYQNqOQDWwkyULRI30kAOSzYwO5PYLC1DjBb+GcnPJBv+2BmYHT988OMbYrQgAcYGEjWMglEwCkbBKMAJAP3jLDUm4vB8AAAAAElFTkSuQmCC","orcid":"","institution":"Royal Military College of Canada","correspondingAuthor":true,"prefix":"","firstName":"Abdellah","middleName":"","lastName":"Chehri","suffix":""},{"id":267410792,"identity":"befdfbcf-70ec-4acf-83ad-08ee11683123","order_by":3,"name":"Atman Jbari","email":"","orcid":"","institution":"Mohammed V University","correspondingAuthor":false,"prefix":"","firstName":"Atman","middleName":"","lastName":"Jbari","suffix":""},{"id":267410793,"identity":"efe5fc5f-b1d6-49c3-80d3-9e1f6242ee23","order_by":4,"name":"Rachid Saadane","email":"","orcid":"","institution":"Hassania School of Public Works","correspondingAuthor":false,"prefix":"","firstName":"Rachid","middleName":"","lastName":"Saadane","suffix":""},{"id":267410794,"identity":"2a7c03c4-2ec5-4752-924f-c4feeee008ce","order_by":5,"name":"Abdeslam Jakimi","email":"","orcid":"","institution":"Université Moulay Ismail de Meknes","correspondingAuthor":false,"prefix":"","firstName":"Abdeslam","middleName":"","lastName":"Jakimi","suffix":""}],"badges":[],"createdAt":"2024-01-15 04:29:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3865389/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3865389/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51674827,"identity":"73b34180-845c-4807-b09e-42bd4e86cbcd","added_by":"auto","created_at":"2024-02-27 04:25:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981785,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3865389/v1_covered_f13ced1b-c437-4523-b63e-b42e50a8ccf4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning-Based Intelligent Smart Embedded Sensors for Automatic Detection and Classification of Neuromuscular Disorders using EMG Signals","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":"IoT-based Body Area Networks, Instantaneous E-healthcare Services, Classification, CompactRIO, Electromyography Signal, Discrete Wavelet Transform, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-3865389/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3865389/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe objective of this work is to create a novel computer-aided health monitoring system for diagnosing neuromuscular disorders (NMDs). 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