Human Activity Recognition for Enhanced Healthcare Monitoring Using Deep Learning

Human Activity Recognition for Enhanced Healthcare Monitoring Using Deep 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 Article Human Activity Recognition for Enhanced Healthcare Monitoring Using Deep Learning Hamza Khan, Fajr Naveed, Zaki Uddin, Khalid Mehmood Cheema, Muhammad Farhan Khan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6790794/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Human Activity Recognition (HAR) is increasingly important due to its potential to transform healthcare by enabling proactive and continuous monitoring of patient activities, facilitating early detection of health risks, and enhancing personalized care. Real-time recognition of human activities can significantly contribute to improved health outcomes by providing critical insights into patient behavior, mobility patterns, and rehabilitation progress.We utilized sensor data collected from hip-mounted accelerometers and gyroscopes provided in the USC-HAD dataset. While our approach focuses on hip-worn data, the resulting models may provide insights for similar deployments in wearable healthcare technologies. Utilizing the USC-HAD dataset, the proposed method achieves high classification accuracy while addressing challenges related to real-world data variability and class imbalance. Experimental results demonstrate the system’s effectiveness in accurately identifying a broad spectrum of activities, crucial for clinical and wellness monitoring scenarios. We discuss how HAR can augment remote patient care, rehabilitation, and personalized health services by enabling real-time tracking of daily activities and movement patterns. The paper concludes with an in-depth consideration of current limitations, potential integration into healthcare infrastructures, and future research directions to further enhance reliability, scalability, and patient-centered applications. The proposed HAR system achieves a high classification accuracy of approximately 98% using advanced LSTM networks, demonstrating its potential effectiveness in clinical and wellness monitoring scenarios. Physical sciences/Engineering/Electrical and electronic engineering Health sciences/Health care Human Activity Recognition Healthcare Monitoring Deep Learning LSTM USC-HAD Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 03 Jul, 2025 Reviews received at journal 28 Jun, 2025 Reviewers agreed at journal 28 Jun, 2025 Reviews received at journal 28 Jun, 2025 Reviewers agreed at journal 21 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviewers invited by journal 13 Jun, 2025 Editor assigned by journal 13 Jun, 2025 Editor invited by journal 13 Jun, 2025 Submission checks completed at journal 12 Jun, 2025 First submitted to journal 31 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. 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-6790794","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":472156056,"identity":"e7f86de1-ade3-48a3-8149-244a88228632","order_by":0,"name":"Hamza Khan","email":"","orcid":"","institution":"National University of Computer and Emerging Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hamza","middleName":"","lastName":"Khan","suffix":""},{"id":472156057,"identity":"c6c55288-a53f-4e3c-9274-20c8b892f9c6","order_by":1,"name":"Fajr Naveed","email":"","orcid":"","institution":"National University of Computer and Emerging 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