Privacy-preserving Human Activity Recognition Using Principal Component-based Wavelet Cnn

Privacy-preserving Human Activity Recognition Using Principal Component-based Wavelet Cnn | 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 Privacy-preserving Human Activity Recognition Using Principal Component-based Wavelet Cnn Nadira Pervin, Tahsina Farah Sanam, Hafiz Imtiaz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4534861/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Sep, 2024 Read the published version in Signal, Image and Video Processing → Version 1 posted 7 You are reading this latest preprint version Abstract Human activity recognition (HAR) is crucial in applications such as smart homes, interactive games, surveillance, security, and healthcare. In recent years, Channel State Information (CSI) data extracted from Wi-Fi signals has garnered significant interest for applications in HAR. This interest stems from CSI's several advantages, including its immunity to illumination variations and environmental disturbances, and the elimination of the need for wearable devices. Despite being widely used, existing HAR system's performance suffers when used in new environments without system improvement or retraining. This constraint can be overcome by gathering and annotating data from various locations, and then re-training the system. However, it is far from ideal from the privacy perspective, as the training algorithms access the data from different privacy-sensitive environments. This motivates us to design a reliable and robust privacy-preserving HAR system. In this work, we introduce a Differentially Private Principal Component-based Wavelet Convolutional Neural Network (DP-PCWCNN) that offers accurate and robust HAR performance across different environments, while preserving strict privacy constraints. We evaluate the performance of our proposed algorithm on a publicly available real dataset and demonstrate that our proposed system closely approximates the non-private system's performance for some parameter choices. Human Activity recognition(HAR) Channel State Information(CSI) Principle Component Analysis(PCA) Wavelet Convolutional Neural Network (WCNN) Differential privacy (DP) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Sep, 2024 Read the published version in Signal, Image and Video Processing → Version 1 posted Editorial decision: Revision requested 13 Jul, 2024 Reviews received at journal 13 Jul, 2024 Reviewers agreed at journal 13 Jul, 2024 Reviewers invited by journal 11 Jul, 2024 Editor assigned by journal 05 Jun, 2024 Submission checks completed at journal 05 Jun, 2024 First submitted to journal 05 Jun, 2024 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-4534861","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314249352,"identity":"4deb85c0-8ad3-4ca9-8a8f-906109cd4cea","order_by":0,"name":"Nadira Pervin","email":"","orcid":"","institution":"Bangladesh University of Engineering and Technology (BUET)","correspondingAuthor":false,"prefix":"","firstName":"Nadira","middleName":"","lastName":"Pervin","suffix":""},{"id":314249353,"identity":"e45c476e-5da7-4d17-96ba-b3660731d963","order_by":1,"name":"Tahsina Farah Sanam","email":"","orcid":"","institution":"Bangladesh University of Engineering and Technology (BUET)","correspondingAuthor":false,"prefix":"","firstName":"Tahsina","middleName":"Farah","lastName":"Sanam","suffix":""},{"id":314249354,"identity":"14770637-612b-4a48-b293-7f30e4144fa0","order_by":2,"name":"Hafiz Imtiaz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie2PvWrDMBCATwjkxU1WZWlfQZmMoeRZFATuEkihS4ZQNHky6eon6awQcJZCVhcvDgHPzlI8tGlP6c9SOy2dOugDnQ7dfegOwOH4hwggpcEIDDB7f/kqyQ6FCqtwVKj4UIj+SbE3x8P4r5TAW5Flc31/2/PW1ayeXULA1bqE+QhL7UqYKDBcFJz5MnpMHyII02isIVNYKtsHM1NjhFVAZsVZvAKRT4YaGC6Yd+yy2YGRVulv45uXwyt2TvcaDieUHAczVuGKUaJt54RoEp9SdrDUohjEvKKDJFN+mFTDdLywSccuG0X3zXPRv7i72tbNfHQeeKqs6yebtP/yDf8Y5WficDgcjj/xBl8cYjCpFONtAAAAAElFTkSuQmCC","orcid":"","institution":"Bangladesh University of Engineering and Technology (BUET)","correspondingAuthor":true,"prefix":"","firstName":"Hafiz","middleName":"","lastName":"Imtiaz","suffix":""}],"badges":[],"createdAt":"2024-06-05 14:39:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4534861/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4534861/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11760-024-03535-0","type":"published","date":"2024-09-02T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64186300,"identity":"d3726f86-a726-44d9-ab17-8cabc94ba736","added_by":"auto","created_at":"2024-09-09 16:26:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1294111,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4534861/v1_covered_62b4e1d1-6710-43da-8c9e-0de1edbff3da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrivacy-preserving Human Activity Recognition Using Principal Component-based Wavelet Cnn\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Human Activity recognition(HAR), Channel State Information(CSI), Principle Component Analysis(PCA), Wavelet Convolutional Neural Network (WCNN), Differential privacy (DP)","lastPublishedDoi":"10.21203/rs.3.rs-4534861/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4534861/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Human activity recognition (HAR) is crucial in applications such as smart homes, interactive games, surveillance, security, and healthcare. In recent years, Channel State Information (CSI) data extracted from Wi-Fi signals has garnered significant interest for applications in HAR. This interest stems from CSI's several advantages, including its immunity to illumination variations and environmental disturbances, and the elimination of the need for wearable devices. Despite being widely used, existing HAR system's performance suffers when used in new environments without system improvement or retraining. This constraint can be overcome by gathering and annotating data from various locations, and then re-training the system. However, it is far from ideal from the privacy perspective, as the training algorithms access the data from different privacy-sensitive environments. This motivates us to design a reliable and robust privacy-preserving HAR system. In this work, we introduce a Differentially Private Principal Component-based Wavelet Convolutional Neural Network (DP-PCWCNN) that offers accurate and robust HAR performance across different environments, while preserving strict privacy constraints. We evaluate the performance of our proposed algorithm on a publicly available real dataset and demonstrate that our proposed system closely approximates the non-private system's performance for some parameter choices.","manuscriptTitle":"Privacy-preserving Human Activity Recognition Using Principal Component-based Wavelet Cnn","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-19 18:10:35","doi":"10.21203/rs.3.rs-4534861/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-14T00:41:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-13T08:16:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105329576028759555838272358651130850314","date":"2024-07-13T08:06:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-11T05:54:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-06T02:06:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-06T02:05:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Signal, Image and Video Processing","date":"2024-06-05T14:38:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"43a9ee06-b682-48fd-9b4c-acd59c832f77","owner":[],"postedDate":"June 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-09T16:18:36+00:00","versionOfRecord":{"articleIdentity":"rs-4534861","link":"https://doi.org/10.1007/s11760-024-03535-0","journal":{"identity":"signal-image-and-video-processing","isVorOnly":false,"title":"Signal, Image and Video Processing"},"publishedOn":"2024-09-02 15:57:11","publishedOnDateReadable":"September 2nd, 2024"},"versionCreatedAt":"2024-06-19 18:10:35","video":"","vorDoi":"10.1007/s11760-024-03535-0","vorDoiUrl":"https://doi.org/10.1007/s11760-024-03535-0","workflowStages":[]},"version":"v1","identity":"rs-4534861","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4534861","identity":"rs-4534861","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}