Multiscale Wavelet Feature Extraction Integrated with CNN for Improved Gesture Prediction

Multiscale Wavelet Feature Extraction Integrated with CNN for Improved Gesture Prediction | 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 Multiscale Wavelet Feature Extraction Integrated with CNN for Improved Gesture Prediction Rohit Sinha, Deepak Kumar, Ravi Kant Prasad, Chhotelal Mahto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7309446/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 This study focuses on the application of multiscale wavelet analysis to hand gesture recognition. A crucial component of human-computer interaction, hand gesture recognition makes it possible for a variety of applications, including assistive technology, robotics, and virtual reality, to have natural and intuitive communication interfaces. For reliable and accurate hand gesture detection, this study proposes a hybrid method that combines convolutional neural networks (CNNs) and multiscale wavelet transforms. The system initially breaks down gesture images into several frequency bands using 2D wavelet transforms, thereby capturing both high-level and low-level data. In order to extract discriminative features across scales and orientations, several wavelet families—such as Haar, Daubechies, Coiflets, and Morlet—are investigated. These wavelet-based features are then fed into a custom CNN architecture for deep learning-based classification. The proposed model is evaluated on a benchmark hand gesture dataset and demonstrates superior performance compared to traditional image-based methods. The results indicate improved accuracy, noise robustness, and generalization across gesture types and users. This study highlights the potential of combining multiscale signal analysis with deep learning for scalable and real-time gesture recognition systems. CNN Wavelet Transforms Hand gesture recognition Haar Daubechies Coiflets and Morlet Full Text Additional Declarations Competing interest reported. Helping in Experimental and Discussion Part Drafting the Research Paper 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. 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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-7309446","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499031801,"identity":"fdd5eff1-304c-422d-aeab-bd68e84754ce","order_by":0,"name":"Rohit Sinha","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rohit","middleName":"","lastName":"Sinha","suffix":""},{"id":499031803,"identity":"b16e4c22-7da1-4ba3-a36a-2e9da181262e","order_by":1,"name":"Deepak Kumar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Kumar","suffix":""},{"id":499031807,"identity":"58f41b83-51c1-4ae5-99e3-3051183d2be9","order_by":2,"name":"Ravi Kant Prasad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABH0lEQVRIie3PMUvDQBTA8evSW17JeiXBfoXL0igE/CoR4aYITplEBCEZPDtb/BIRJ7ccB3WJ3YSEOhQKcenQIEiHKuZEqkiaroL3n97B+3F3COl0fzRxth7bLvSwOtLdrYR8EWbZPFGENJLWDyJdlHlq2kycKLXF1cnTqWPK2WIZMGgNnx/j5TFBRnTh1REr9amIRwXZG7B+l49dwKYf5Lx6GEkf4jpCEPPEtC0JTVEfdcLqlmufZVARSo7qiVFU5F0R/FK+hRJQnrJ81UTIYSJuQkWAmh1FMjyaNN5CZokYDmT3jkNgWmMGNvfbE4sS2PgX4+C85K/ScADflvPA3e/h+yKfr9wdI7qsJevo9wifMzSu/yJ4unVbp9Pp/lUfPcNnRkhWRFEAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ravi","middleName":"Kant","lastName":"Prasad","suffix":""},{"id":499031811,"identity":"9f4a72dd-0fed-48d7-99ea-b530c7d8dc9a","order_by":3,"name":"Chhotelal Mahto","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chhotelal","middleName":"","lastName":"Mahto","suffix":""}],"badges":[],"createdAt":"2025-08-06 11:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7309446/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7309446/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93451220,"identity":"47279897-497f-4b33-b960-3f59d57a3108","added_by":"auto","created_at":"2025-10-14 03:31:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981305,"visible":true,"origin":"","legend":"","description":"","filename":"HandgesturesdetectionusingmultiscalewaveletsintegratedtoCNN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7309446/v1_covered_36b2200c-caa3-47eb-aadc-b1a190b00786.pdf"}],"financialInterests":"Competing interest reported. 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A crucial component of human-computer interaction, hand gesture recognition makes it possible for a variety of applications, including assistive technology, robotics, and virtual reality, to have natural and intuitive communication interfaces. For reliable and accurate hand gesture detection, this study proposes a hybrid method that combines convolutional neural networks (CNNs) and multiscale wavelet transforms. The system initially breaks down gesture images into several frequency bands using 2D wavelet transforms, thereby capturing both high-level and low-level data. In order to extract discriminative features across scales and orientations, several wavelet families—such as Haar, Daubechies, Coiflets, and Morlet—are investigated. These wavelet-based features are then fed into a custom CNN architecture for deep learning-based classification. 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