A motion vector sensor-enabled 3D spinal morphology monitoring suit for intelligent scoliosis identification

A motion vector sensor-enabled 3D spinal morphology monitoring suit for intelligent scoliosis identification | 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 A motion vector sensor-enabled 3D spinal morphology monitoring suit for intelligent scoliosis identification Yue Zhang, Liangxu Xu, Guilin Chen, Hongjun Liu, Xuan Zhao, Chao Yao, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9438984/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 Early identification of scoliosis is highly significant for timely intervention and efficient management of the disease. However, conventional static screening approaches rely heavily on subjective assessments, resulting in limited diagnostic accuracy and insufficient continuity. Here, we propose a motion vector sensor-enabled 3D spinal morphology monitoring suit for intelligent identification and continuous monitoring of scoliosis. By orderly aligning elastic conductive fibers, highly anisotropic soft meshes are customized and achieve dual responses to motion amplitude and direction. The sensors exhibit a minimal directional resolution of 2° while maintaining a linear response up to 100% strain. The suit integrates motion sensors positioned at symmetrical body locations to quantify 3D spinal motions via precise measurement of skin-surface deformation amplitude and direction. Dynamic scoliosis identification is achieved through machine learning-powered kinematic symmetry evaluation of 3D spinal motions. The system demonstrates accuracies of 100% for scoliosis detection, 97.7% for curvature degree quantification, and 93.37% for curvature location identification, rigorously validated on a multi-center dataset from four hospitals and one school-based screening program. This work provides a radiation-free alternative for scoliosis screening, with potential to facilitate lifelong monitoring and management of the condition. Physical sciences/Materials science/Materials for devices/Sensors and biosensors Physical sciences/Engineering/Biomedical engineering Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryinformation.pdf Supplementary information SupplementaryMovie1.mp4 Supplementary Movie 1 SupplementaryMovie2.mp4 Supplementary Movie 2 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. 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-9438984","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":626041969,"identity":"4787fa2f-a3d6-4125-9050-3e20ffa456cb","order_by":0,"name":"Yue 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