Computational intelligence for soft strain sensor sustainability

Computational intelligence for soft strain sensor sustainability | 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 Article Computational intelligence for soft strain sensor sustainability Xiaonan Wang, Jiali Li, Haitao Yang, Lanjing Wang, Nungsiong Lai, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7513990/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The ever-growing demand of soft strain sensors in ubiquitous electronics poses urgent need of sustainability innovations to reduce resource waste. Besides the efforts toward sustainable device manufacture, it is as important to build performance sustainability of soft sensor with reliable sensing signals over its lifecycle, yet facing pervasive predicaments of nonlinearity, hysteresis, cycling attenuation, and batch inconsistency. Herein, instead of empirical experiments via material or structural engineering, we propose an efficient computational sustainability framework to comprehensively address these signal predicaments by utilizing hierarchical domain-constrained machine learning (ML) models. Using an eco-friendly carbon waste-based strain sensor as a case study, domain intuited ML models are developed and show both high computation efficiency and learning accuracy in terms of real-time sensing signal calibration and signal compensation of the developed sensor, which automatically enhances its unsatisfied sensing performance to equivalently linear, non-hysteresis, long-term stable, and batch consistent one without trial-and-error experiments. As final demonstrations, the ML-driven computational models are capable of expanding sensor’s reliable working lifetime for > 3,000 times than its own counterpart for multiple robotic tasks, facilitating their long-term usages during practical applications to attain the sustainable development goal. Physical sciences/Materials science/Soft materials Physical sciences/Materials science/Theory and computation/Computational methods Physical sciences/Materials science/Materials for devices Full Text Additional Declarations There is NO Competing Interest. Supplementary Files 01SIFinal0901.pdf Supplementary Information softquadrupedrobottrajectoryestimating.mp4 Soft quadruped robot trajectory estimating flexiblerobotarmtracking.mp4 Flexible robot arm tracking Cite Share Download PDF Status: Under Review 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. 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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-7513990","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":514248478,"identity":"9c829a6e-71ed-4b79-9134-2368fbedb844","order_by":0,"name":"Xiaonan 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