{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T14:13:57Z","timestamp":1789827237114,"version":"4.0.1"},"reference-count":23,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T00:00:00Z","timestamp":1723680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China Special Program","award":["62341311"],"award-info":[{"award-number":["62341311"]}]},{"name":"National Natural Science Foundation of China Special Program","award":["2022-RC-7"],"award-info":[{"award-number":["2022-RC-7"]}]},{"name":"Lanzhou Municipal Talent Innovation and Entrepreneurship Project","award":["62341311"],"award-info":[{"award-number":["62341311"]}]},{"name":"Lanzhou Municipal Talent Innovation and Entrepreneurship Project","award":["2022-RC-7"],"award-info":[{"award-number":["2022-RC-7"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the challenges in recognizing various types of falls, which often exhibit high similarity and are difficult to distinguish, this paper proposes a human fall classification system based on the SE-Residual Concatenate Network (SE-RCNet) with adaptive weighted fusion. First, we designed the innovative SE-RCNet network, incorporating SE modules after dense and residual connections to automatically recalibrate feature channel weights and suppress irrelevant features. Subsequently, this network was used to train and classify three types of radar images: time\u2013distance images, time\u2013distance images, and distance\u2013distance images. By adaptively fusing the classification results of these three types of radar images, we achieved higher action recognition accuracy. Experimental results indicate that SE-RCNet achieved F1-scores of 94.0%, 94.3%, and 95.4% for the three radar image types on our self-built dataset. After applying the adaptive weighted fusion method, the F1-score further improved to 98.1%.<\/jats:p>","DOI":"10.3390\/s24165294","type":"journal-article","created":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T09:29:41Z","timestamp":1723714181000},"page":"5294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Human Fall Detection with Ultra-Wideband Radar and Adaptive Weighted Fusion"],"prefix":"10.3390","volume":"24","author":[{"given":"Ling","family":"Huang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anfu","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengjie","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huifeng","family":"An","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, X., Ellul, J., and Azzopardi, G. 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