An Efficient WiFi CSI-Based Multi-Task Modeling Method for Indoor Activity Recognition and Localization: LBA-TCN

An Efficient WiFi CSI-Based Multi-Task Modeling Method for Indoor Activity Recognition and Localization: LBA-TCN | 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 An Efficient WiFi CSI-Based Multi-Task Modeling Method for Indoor Activity Recognition and Localization: LBA-TCN Jiayao He, Kun Zhang, Bing Zheng, Keliu Long, Yu Zhou, Yiguo Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6896851/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract With the rapid development of the Internet of Things (IoT) and wireless sensing technologies, contactless perception has become a key enabler for intelligent environments. WiFi Channel State Information (CSI), due to its advantages such as obstacle penetration, low cost, and no need for additional hardware, has been widely applied in tasks including activity recognition, localization, and vital sign monitoring. In this context, how to efficiently utilize CSI data for joint multi-task perception has become an important research focus in the field of wireless intelligent sensing. This paper proposes a multi-task deep learning model, LBA-TCN (Lightweight Bahdanau Attention Temporal Convolutional Network), which integrates multi-scale convolution, temporal modeling, and attention mechanisms for simultaneous activity recognition and indoor localization. The model employs three convolutional branches with different receptive fields to extract multi-scale spatial features and incorporates a Temporal Convolutional Network (TCN) to capture temporal dependencies in CSI sequences. A lightweight additive attention mechanism is further designed to enhance the representation of key temporal features. Experimental results show that LBA-TCN demonstrates strong stability and generalization in multi-class recognition tasks, verifying its potential in WiFi-based multi-task indoor perception applications. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology multi-scale convolution lightweight additive attention mechanism temporal convolutional network (TCN) WiFi CSI multi-task learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Jul, 2025 Reviews received at journal 07 Jul, 2025 Reviews received at journal 06 Jul, 2025 Reviewers agreed at journal 04 Jul, 2025 Reviewers agreed at journal 04 Jul, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers invited by journal 17 Jun, 2025 Editor assigned by journal 17 Jun, 2025 Editor invited by journal 17 Jun, 2025 Submission checks completed at journal 16 Jun, 2025 First submitted to journal 15 Jun, 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-6896851","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":473037880,"identity":"035ff036-2479-4ad2-90de-9b3d8e6dec38","order_by":0,"name":"Jiayao He","email":"","orcid":"","institution":"Hainan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jiayao","middleName":"","lastName":"He","suffix":""},{"id":473037882,"identity":"debe95fa-631d-45d8-beab-ae0ca1875bdb","order_by":1,"name":"Kun 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WiFi Channel State Information (CSI), due to its advantages such as obstacle penetration, low cost, and no need for additional hardware, has been widely applied in tasks including activity recognition, localization, and vital sign monitoring. In this context, how to efficiently utilize CSI data for joint multi-task perception has become an important research focus in the field of wireless intelligent sensing. This paper proposes a multi-task deep learning model, LBA-TCN (Lightweight Bahdanau Attention Temporal Convolutional Network), which integrates multi-scale convolution, temporal modeling, and attention mechanisms for simultaneous activity recognition and indoor localization. The model employs three convolutional branches with different receptive fields to extract multi-scale spatial features and incorporates a Temporal Convolutional Network (TCN) to capture temporal dependencies in CSI sequences. 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