Human Fall Detection Based on the HDC-RepLKNet Network

Human Fall Detection Based on the HDC-RepLKNet Network | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 26 April 2025 V1 Latest version Share on Human Fall Detection Based on the HDC-RepLKNet Network Authors : Linfeng Bai , heng zheng 0009-0005-0914-710X [email protected] , Kai Lu , xiangqun Zhang , genyuan Du , and chenxi Zhao Authors Info & Affiliations https://doi.org/10.22541/au.174564923.31335634/v1 201 views 125 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Addressing elderly health monitoring amid accelerating population aging, this study proposes a non-contact fall detection method using millimeter-wave radar and an improved HDC-RepLKNet network. The TI IWR1843 radar captures human motion echoes, with fused Range-Time Maps (RTM) and Doppler-Time Maps (DTM) providing comprehensive motion characterization. The enhanced network integrates an attention mechanism for refined feature extraction and reduces computational complexity for low-power devices. Tested on a dataset of six actions (three falls, three non-falls), the method achieves 99.92% detection accuracy with strong generalization. This work advances non-contact fall detection and posture recognition, offering a practical solution for elderly care. Supplementary Material File (figure.rar) Download 1.77 MB Information & Authors Information Version history V1 Version 1 26 April 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords image classification millimetre wave radar neural nets Authors Affiliations Linfeng Bai Henan Institute of Science and Technology View all articles by this author heng zheng 0009-0005-0914-710X [email protected] Henan Institute of Science and Technology View all articles by this author Kai Lu Xuchang University View all articles by this author xiangqun Zhang Xuchang University View all articles by this author genyuan Du xu chang xue yuan View all articles by this author chenxi Zhao Xuchang University View all articles by this author Metrics & Citations Metrics Article Usage 201 views 125 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Linfeng Bai, heng zheng, Kai Lu, et al. Human Fall Detection Based on the HDC-RepLKNet Network. Authorea . 26 April 2025. DOI: https://doi.org/10.22541/au.174564923.31335634/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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