Comparative Evaluation of Commercial and Laboratory-Developed Stretchable Sensors for Facial Expression Recognition Using Machine Learning

Comparative Evaluation of Commercial and Laboratory-Developed Stretchable Sensors for Facial Expression Recognition Using Machine Learning | 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 Comparative Evaluation of Commercial and Laboratory-Developed Stretchable Sensors for Facial Expression Recognition Using Machine Learning Chowdhury Mohammad Masum Refat, Norsinnira Zainul Azlan, Anis Nurashikin Nordin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8769963/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 Facial expression recognition (FER) is an important component of human--machine interaction, healthcare monitoring, and rehabilitation robotics. Conventional FER systems are predominantly image-based and rely on computer vision techniques, which require high computational resources and are sensitive to environmental conditions such as illumination and occlusion. To address these limitations, this study investigates a wearable sensing approach for FER using stretchable sensors. A comparative evaluation is conducted between a commercially available capacitance-based stretchable sensor and a laboratory-developed resistance-based stretchable sensor fabricated at the International Islamic University Malaysia (IIUM). Time-series data corresponding to four facial expressions---neutral, happy, sad, and disgust---were collected from 30 participants using multiple sensor placements on the face. Statistical features, including mean and standard deviation, were extracted from the sensor signals and used to train and test five supervised machine-learning classifiers: k-nearest neighbor, decision tree, support vector machine, logistic regression, and random forest. Experimental results demonstrate that the commercial sensor achieves higher overall recognition performance, with the random forest classifier yielding an average F1-score of 96%, compared with 90% for the laboratory-developed sensor. The superior performance of the commercial sensor is attributed to its higher sampling rate and greater signal stability. Nevertheless, the laboratory-developed sensor offers advantages in terms of cost, design flexibility, and the ability to measure both stretch and compression. The findings confirm the feasibility of stretchable sensors for facial expression recognition and highlight the potential of laboratory-developed sensors for cost-effective FER systems, particularly in applications such as rehabilitation robotics and human-centered assistive technologies. Facial expression recognition stretchable sensors wearable sensors machine learning time-series classification human--machine interaction Full Text Additional Declarations The authors declare no competing interests. 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-8769963","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584693348,"identity":"01f180b0-a4da-48b4-986a-f2c0f3ac35e9","order_by":0,"name":"Chowdhury Mohammad Masum Refat","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Chowdhury","middleName":"Mohammad Masum","lastName":"Refat","suffix":""},{"id":584693688,"identity":"cb34b39c-13ff-4503-9a10-53cbada8922b","order_by":1,"name":"Norsinnira Zainul Azlan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Norsinnira","middleName":"Zainul","lastName":"Azlan","suffix":""},{"id":584693689,"identity":"ec233501-6a81-4666-9de6-2a03426b2c30","order_by":2,"name":"Anis Nurashikin Nordin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anis","middleName":"Nurashikin","lastName":"Nordin","suffix":""},{"id":584693690,"identity":"28b2edbb-0650-4d5c-aae4-a832f23e5adc","order_by":3,"name":"Noor Amalina Ramli","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Noor","middleName":"Amalina","lastName":"Ramli","suffix":""}],"badges":[],"createdAt":"2026-02-03 02:17:31","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8769963/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8769963/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101881596,"identity":"8843e205-b83b-4081-a03e-f607d4de368d","added_by":"auto","created_at":"2026-02-04 15:13:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8535143,"visible":true,"origin":"","legend":"","description":"","filename":"ComparativeEvaluationofCommercialandLaboratoryDevelopedStretchableSensorsforFacialExpressionRecognitionUsingMachineLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8769963/v1_covered_f6f30966-1ac6-485f-9b27-428b2f47f8bb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eComparative Evaluation of Commercial and Laboratory-Developed Stretchable Sensors for Facial Expression Recognition Using Machine Learning\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Osaka University","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":"Facial expression recognition, stretchable sensors; wearable sensors, machine learning, time-series classification, human--machine interaction","lastPublishedDoi":"10.21203/rs.3.rs-8769963/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8769963/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFacial expression recognition (FER) is an important component of human--machine interaction, healthcare monitoring, and rehabilitation robotics. Conventional FER systems are predominantly image-based and rely on computer vision techniques, which require high computational resources and are sensitive to environmental conditions such as illumination and occlusion. To address these limitations, this study investigates a wearable sensing approach for FER using stretchable sensors. A comparative evaluation is conducted between a commercially available capacitance-based stretchable sensor and a laboratory-developed resistance-based stretchable sensor fabricated at the International Islamic University Malaysia (IIUM). Time-series data corresponding to four facial expressions---neutral, happy, sad, and disgust---were collected from 30 participants using multiple sensor placements on the face. Statistical features, including mean and standard deviation, were extracted from the sensor signals and used to train and test five supervised machine-learning classifiers: k-nearest neighbor, decision tree, support vector machine, logistic regression, and random forest. Experimental results demonstrate that the commercial sensor achieves higher overall recognition performance, with the random forest classifier yielding an average F1-score of 96%, compared with 90% for the laboratory-developed sensor. The superior performance of the commercial sensor is attributed to its higher sampling rate and greater signal stability. Nevertheless, the laboratory-developed sensor offers advantages in terms of cost, design flexibility, and the ability to measure both stretch and compression. The findings confirm the feasibility of stretchable sensors for facial expression recognition and highlight the potential of laboratory-developed sensors for cost-effective FER systems, particularly in applications such as rehabilitation robotics and human-centered assistive technologies.\u003c/p\u003e","manuscriptTitle":"Comparative Evaluation of Commercial and Laboratory-Developed Stretchable Sensors for Facial Expression Recognition Using Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 05:03:44","doi":"10.21203/rs.3.rs-8769963/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"4102575c-2f9a-4136-acf4-5c57d8aab0bb","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T05:03:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 05:03:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8769963","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8769963","identity":"rs-8769963","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}