Automatic pain identification classification in older patients with hip fracture based on multi-modal information fusion

Automatic pain identification classification in older patients with hip fracture based on multi-modal information fusion | 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 Automatic pain identification classification in older patients with hip fracture based on multi-modal information fusion Shuang Yang, Wen Luo, Tao Yang, Xiaoying Chen, Siyi Shen, Lei Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6581266/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Objective : Considering the disadvantages of uni-modal pain recognition, this study aimed to develop a pain recognition classification system for older patients with hip fractures using multi-modal information fusion. Methods : Based on the Residual Network 50 automatic recognition classification system for pain expression, this study used the VGGish network and the bi-directional long short-term memory (BiLSTM) network to establish a pain speech recognition classification system, and the channel attention mechanism was used for optimization. Finally, a weighted-sum mechanism was used to integrate the two uni-modal pain recognition classification systems to form a multi-modal pain recognition classification system. A self-built multi-modal pain database was used for model training and validation, and the training set was allocated in an 8:2 ratio. The final model was tested on the BioVid heat pain dataset. Results : The VGGish model optimized by a LSTM network and the channel attention mechanism were trained on a hip fracture pain dataset, and the accuracy of the model was maintained at 80% after 500 iterations. The model was tested in BioVid heat pain database, Pain 2 to 4 grades, and the confusion matrix test had an accuracy of 85% for Pain 4 grade. Conclusion : This is the first study to establish an automatic multi-modal pain expression recognition classification system based on facial expression and audio information, and to clinically verify the feasibility of this system. Biological sciences/Computational biology and bioinformatics Health sciences/Medical research pain recognition deep learning transfer learning multi-modal fusion Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 May, 2025 Reviews received at journal 24 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviews received at journal 10 May, 2025 Reviewers agreed at journal 07 May, 2025 Reviewers invited by journal 07 May, 2025 Editor assigned by journal 07 May, 2025 Editor invited by journal 07 May, 2025 Submission checks completed at journal 06 May, 2025 First submitted to journal 02 May, 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. 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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-6581266","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":454719305,"identity":"1392a524-dff1-448b-b33e-13caa8f15b7c","order_by":0,"name":"Shuang Yang","email":"","orcid":"","institution":"Tianjin Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Yang","suffix":""},{"id":454719306,"identity":"f544711e-5da1-4929-9e33-bd4b203ae939","order_by":1,"name":"Wen 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transfer learning, multi-modal fusion","lastPublishedDoi":"10.21203/rs.3.rs-6581266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6581266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: Considering the disadvantages of uni-modal pain recognition, this study aimed to develop a pain recognition classification system for older patients with hip fractures using multi-modal information fusion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Based on the Residual Network 50 automatic recognition classification system for pain expression, this study used the VGGish network and the bi-directional long short-term memory (BiLSTM) network to establish a pain speech recognition classification system, and the channel attention mechanism was used for optimization. Finally, a weighted-sum mechanism was used to integrate the two uni-modal pain recognition classification systems to form a multi-modal pain recognition classification system. A self-built multi-modal pain database was used for model training and validation, and the training set was allocated in an 8:2 ratio. The final model was tested on the BioVid heat pain dataset.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The VGGish model optimized by a LSTM network and the channel attention mechanism were trained on a hip fracture pain dataset, and the accuracy of the model was maintained at 80% after 500 iterations. 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