Lightweight and Adaptive Sitting Posture Recognition with Acoustic Signals

Lightweight and Adaptive Sitting Posture Recognition with Acoustic Signals | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 11 April 2025 V1 Latest version Share on Lightweight and Adaptive Sitting Posture Recognition with Acoustic Signals Authors : Hongliang Bi 0009-0008-5094-6001 , Chen Li , Zixiang Wei , Shuyu Song , Xukun Yu , Weitao Xiong , and Xiaotao Xu [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174438382.29245548/v1 190 views 156 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract With increasing computer-based work, prolonged poor sitting posture can result in negative health effects such as scoliosis. However, current sitting posture detection systems often require the purchase of additional hardware. The camera-based detection system may compromise user privacy and be impacted by varying lighting conditions. In this paper, we propose a solution to realize a sitting posture detection system derived from acoustic signals generated by smartphones. Firstly, the acoustic signals of different sitting postures are obtained through the built-in speaker and microphone of the smartphone. Then an innovative signal segmentation technique based on the adaptive threshold is designed to extract the signals, followed by the creation of a deep learning model for posture recognition. To meet the demand for a lightweight model, a knowledge distillation compression technique is used to compress the model while maintaining its accuracy. The experimental results prove that our sitting posture detection model has good effectiveness and robustness, making it more universal. Supplementary Material File (wileynjdv5_ama.pdf) Download 11.77 MB Information & Authors Information Version history V1 Version 1 11 April 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords adaptive lightweight sitting posture recognition ultrasonic signal Authors Affiliations Hongliang Bi 0009-0008-5094-6001 China University of Mining and Technology View all articles by this author Chen Li Northwestern Polytechnical University View all articles by this author Zixiang Wei Northwestern Polytechnical University View all articles by this author Shuyu Song Northwestern Polytechnical University View all articles by this author Xukun Yu Northwestern Polytechnical University View all articles by this author Weitao Xiong Northwestern Polytechnical University View all articles by this author Xiaotao Xu [email protected] National University of Defense Technology School of Information and Communication View all articles by this author Metrics & Citations Metrics Article Usage 190 views 156 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Hongliang Bi, Chen Li, Zixiang Wei, et al. Lightweight and Adaptive Sitting Posture Recognition with Acoustic Signals. Authorea . 11 April 2025. DOI: https://doi.org/10.22541/au.174438382.29245548/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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