Early Detection and Prevention of Occupational Diseases Related to 'Excessive Workload': Analyzing of Scientific Researchers' Data in Universities Utilizing Deep Learning Models

Early Detection and Prevention of Occupational Diseases Related to 'Excessive Workload': Analyzing of Scientific Researchers' Data in Universities Utilizing Deep Learning Models | 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 Early Detection and Prevention of Occupational Diseases Related to 'Excessive Workload': Analyzing of Scientific Researchers' Data in Universities Utilizing Deep Learning Models Xinyi Yang, Lu Yu, Hengjian Wei, Le Xue, Wenjing Shen, Huanping Wei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5309718/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 As a typical representative of a high-stress occupational group, university researchers are exposed to significant risks associated with ‘excessive workload’. Continuous engagement in intense mental labor leads to the accumulation of physical and mental fatigue, which ultimately increases the susceptibility to cardiovascular and heart diseases. Identifying and monitoring sensitive physiological indicators associated with ‘excessive workload’ is considered an effective strategy to reduce the risks. In this study, an experiment was conducted to build a dataset for researchers using a selection of physiologically sensitive indicators. The dataset including photoplethysmography (PPG) signals, facial behavioral attributes and head posture feature parameters. Six deep learning models and three machine learning models were used in this study for analysis. The results show that all constructed models exhibit excellent performance. Notably, the PSO-CNN-LSTM and MFO-CNN-BiLSTM models show unrivalled accuracy and robustness in the classification task, with prediction accuracies of 99.62% and 99.76%. Respectively, along with a stable AUC value of over 0.99. This highlights their ability to accurately predict the risk of ‘excessive workload’ related diseases in humans. This study provides new insights into the prevention and management of occupational diseases, enhancing occupational health management. Health sciences/Health occupations Health sciences/Risk factors Occupational related diseases Deep learning model University scientific researchers PPG signal Ergonomics assessment Full Text Additional Declarations No competing interests reported. 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. 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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-5309718","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":380796789,"identity":"2d94f7e2-60bc-4842-af11-38e61889854a","order_by":0,"name":"Xinyi Yang","email":"","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Yang","suffix":""},{"id":380796790,"identity":"5514f80b-0c60-40c0-8d4a-a04c43f18767","order_by":1,"name":"Lu 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Continuous engagement in intense mental labor leads to the accumulation of physical and mental fatigue, which ultimately increases the susceptibility to cardiovascular and heart diseases. Identifying and monitoring sensitive physiological indicators associated with \u0026lsquo;excessive workload\u0026rsquo; is considered an effective strategy to reduce the risks. In this study, an experiment was conducted to build a dataset for researchers using a selection of physiologically sensitive indicators. The dataset including photoplethysmography (PPG) signals, facial behavioral attributes and head posture feature parameters. Six deep learning models and three machine learning models were used in this study for analysis. The results show that all constructed models exhibit excellent performance. Notably, the PSO-CNN-LSTM and MFO-CNN-BiLSTM models show unrivalled accuracy and robustness in the classification task, with prediction accuracies of 99.62% and 99.76%. 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