Optimizing CNN Hyperparameters for Enhanced sEMG Signal Classification using D-Optimal Design

Optimizing CNN Hyperparameters for Enhanced sEMG Signal Classification using D-Optimal Design | 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 Optimizing CNN Hyperparameters for Enhanced sEMG Signal Classification using D-Optimal Design Arturo A. Marquez Carranza, Moises Arredondo-Velazquez, Benito de Celis-Alonso, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6978265/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Oct, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted 4 You are reading this latest preprint version Abstract The effectiveness of Convolutional Neural Networks (CNNs) in classifying hand gestures from surface electromyography (sEMG) signals is significantly influenced by the meticulous selection of hyperparameters. However, the impact of these structural hyperparameters on classification accuracy remains underexplored. This study investigates five CNN hyperparameters through the implementation of a D-optimal experimental design. This methodology enables the systematic identification of efficient combinations while minimizing the number of experiments necessary for evaluation. Additionally, it allows for a thorough assessment of the individual contributions of each factor to accuracy and inference time, thereby highlighting the most influential parameters. Experimental trials were conducted using three distinct subsets from a public sEMG signal database (NinaPro). The findings indicate that the convolution type exerts the most significant influence on accuracy, closely followed by the number of parallel layers and, to a lesser extent, the number of sequential layers. Notably, one of the identified configurations attained an accuracy of 98.62% \(\pm\) 0.84 on data obtained from subjects with transradial amputations at distinct levels. Furthermore, the most optimized models demonstrated inference times of below 200 ms on a standard processor, demonstrating their potential applicability in real-time environments. Convolutional Neural Network (CNN) D-Optimum Design Surface Electromyography (sEMG) Hyperparameters Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Oct, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted Editorial decision: Revision requested 01 Jul, 2025 Editor assigned by journal 26 Jun, 2025 Submission checks completed at journal 26 Jun, 2025 First submitted to journal 25 Jun, 2025 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-6978265","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":479323575,"identity":"bdbe2f08-3578-4e55-9fee-2644cb5e7341","order_by":0,"name":"Arturo A. Marquez Carranza","email":"","orcid":"","institution":"Meritorious Autonomous University of Puebla","correspondingAuthor":false,"prefix":"","firstName":"Arturo","middleName":"A. 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