Restoring a Sense of Touch: A Single Sensor Wearable Haptic Feedback System Based on Deep Learning and Arm Kinematics

Restoring a Sense of Touch: A Single Sensor Wearable Haptic Feedback System Based on Deep Learning and Arm Kinematics | 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 Restoring a Sense of Touch: A Single Sensor Wearable Haptic Feedback System Based on Deep Learning and Arm Kinematics Johnny Hazboun, Andres Torres, Mo Rastgaar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8252665/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Upper-limb prosthesis users usually lack comprehensive feedback or touch. This significantly hinders their ability to interact with their environment and have normal lives. This research involves the development and evaluation of a low-cost, non-invasive and easy to implement, wearable haptic feedback system. The system seeks to restore a sense of touch by interpreting arm movements through a wrist-worn 6-axis Inertial Measurement Unit (IMU). The setup is meant to be universal and to be used with most other upper-limb prosthetics to give sensory feedback to the user. Training and testing data were obtained from ten subjects performing a series of paired touch and non-touch activities. To perform real-time touch event classifications from the IMU data, various machine learning models were developed and evaluated. Multiple light-weight models were evaluated, including Logistic Regression, Support Vector Machines, Random Forest, and 1D-CNN. The best model, a 1D-CNN model, achieved 99.1% accuracy in classifying touch events of various different types. That model learned features directly from windowed time-series data and was trained using a combination of a partial Synthetic Minority Over-sampling Technique (SMOTE) and a Focal Loss function to prioritize the minority "touch" class. Model results improved when trained on initial contact instance rather than the full contact duration. These lightweight models were then successfully deployed on a Teensy 4.1 microcontroller as a proof of concept for the feasibility of using deep learning on IMU data for real-time sensory substitution. Smart Prosthetics Machine Learning Embedded Systems Wearables Haptic Feedback Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Jan, 2026 Reviews received at journal 25 Dec, 2025 Reviews received at journal 20 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers invited by journal 10 Dec, 2025 Editor assigned by journal 03 Dec, 2025 Submission checks completed at journal 03 Dec, 2025 First submitted to journal 01 Dec, 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-8252665","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":560137036,"identity":"8c7e2acc-ad4d-4fe3-9c62-b14f4a69757b","order_by":0,"name":"Johnny Hazboun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACxhkMDMwgBj+I4AGLJRCpRbKBWC0MElAtBgeI1cI8u/nx54KKw3nGt3vMPrxts2HgZ88xwO+wOccMjGecOVxsdueM8cy5bWkMkj1vCGiZkWCQzNt2OHHbjRxjZiCDweAGIVtmpH84DNKyeQZYy38Ge8JacgybQVo2SIC1HGAwkCCspZh5xpn0xBl3jhUzzjmXzCNx5lkBXi2GM9I3A0PMOrF/dvNmhjdldnL87ckb8GtpgLEkIBQPXuUgIA9nSRBUOwpGwSgYBSMVAAAn3kdkqBypCgAAAABJRU5ErkJggg==","orcid":"","institution":"Purdue University West Lafayette","correspondingAuthor":true,"prefix":"","firstName":"Johnny","middleName":"","lastName":"Hazboun","suffix":""},{"id":560137037,"identity":"4f480bae-8d0d-4ae4-9282-4faff6bf1a38","order_by":1,"name":"Andres Torres","email":"","orcid":"","institution":"Purdue University West Lafayette","correspondingAuthor":false,"prefix":"","firstName":"Andres","middleName":"","lastName":"Torres","suffix":""},{"id":560137040,"identity":"5762d2f2-f6d7-4a5b-93c2-80708298818e","order_by":2,"name":"Mo Rastgaar","email":"","orcid":"","institution":"Purdue University West Lafayette","correspondingAuthor":false,"prefix":"","firstName":"Mo","middleName":"","lastName":"Rastgaar","suffix":""}],"badges":[],"createdAt":"2025-12-01 16:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8252665/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8252665/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98209104,"identity":"32e86210-e9c2-4735-8426-508b0119dd55","added_by":"auto","created_at":"2025-12-15 09:12:58","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5310,"visible":true,"origin":"","legend":"","description":"","filename":"43a54b9693ff428fa2ad3a25b27bb111.json","url":"https://assets-eu.researchsquare.com/files/rs-8252665/v1/f01e0c0b1d6b657ec88e82a0.json"},{"id":98430819,"identity":"7d6cea58-49ce-4b53-9aa1-caabe6c39805","added_by":"auto","created_at":"2025-12-17 16:46:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":872531,"visible":true,"origin":"","legend":"","description":"","filename":"FinalWIthDeclarationsHapticFeedbackSpringerNatureSubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8252665/v1_covered_670d9973-ca82-4f71-a3f6-735b21366828.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eRestoring a Sense of Touch: A Single Sensor Wearable Haptic Feedback System Based on Deep Learning and Arm Kinematics\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":"discover-robotics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Robotics](https://link.springer.com/journal/44430)","snPcode":"44430","submissionUrl":"https://submission.springernature.com/new-submission/44430/3","title":"Discover Robotics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Unsupported Journal","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Smart Prosthetics, Machine Learning, Embedded Systems, Wearables, Haptic Feedback","lastPublishedDoi":"10.21203/rs.3.rs-8252665/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8252665/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Upper-limb prosthesis users usually lack comprehensive feedback or touch. This significantly hinders their ability to interact with their environment and have normal lives. This research involves the development and evaluation of a low-cost, non-invasive and easy to implement, wearable haptic feedback system. The system seeks to restore a sense of touch by interpreting arm movements through a wrist-worn 6-axis Inertial Measurement Unit (IMU). The setup is meant to be universal and to be used with most other upper-limb prosthetics to give sensory feedback to the user. Training and testing data were obtained from ten subjects performing a series of paired touch and non-touch activities. To perform real-time touch event classifications from the IMU data, various machine learning models were developed and evaluated. Multiple light-weight models were evaluated, including Logistic Regression, Support Vector Machines, Random Forest, and 1D-CNN. The best model, a 1D-CNN model, achieved 99.1% accuracy in classifying touch events of various different types. That model learned features directly from windowed time-series data and was trained using a combination of a partial Synthetic Minority Over-sampling Technique (SMOTE) and a Focal Loss function to prioritize the minority \"touch\" class. Model results improved when trained on initial contact instance rather than the full contact duration. These lightweight models were then successfully deployed on a Teensy 4.1 microcontroller as a proof of concept for the feasibility of using deep learning on IMU data for real-time sensory substitution.","manuscriptTitle":"Restoring a Sense of Touch: A Single Sensor Wearable Haptic Feedback System Based on Deep Learning and Arm Kinematics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 09:12:49","doi":"10.21203/rs.3.rs-8252665/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-07T09:17:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-25T10:23:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-20T08:36:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292380097600263373744332731091327743661","date":"2025-12-19T06:07:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308868960561971904575898539536682058680","date":"2025-12-12T03:05:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T09:59:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-03T13:26:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-03T13:23:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Robotics","date":"2025-12-01T16:39:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-robotics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Robotics](https://link.springer.com/journal/44430)","snPcode":"44430","submissionUrl":"https://submission.springernature.com/new-submission/44430/3","title":"Discover Robotics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Unsupported Journal","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c514f1d6-dfa3-4a5a-87fd-74378408583b","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T07:12:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 09:12:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8252665","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8252665","identity":"rs-8252665","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}