{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:02:29Z","timestamp":1785402149915,"version":"3.56.0"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004239","name":"Poznan University of Technology","doi-asserted-by":"publisher","award":["0211\/SBAD\/0226"],"award-info":[{"award-number":["0211\/SBAD\/0226"]}],"id":[{"id":"10.13039\/501100004239","id-type":"DOI","asserted-by":"publisher"}]},{"award":["0211\/SBAD\/0226"],"award-info":[{"award-number":["0211\/SBAD\/0226"]}],"id":[{"id":"https:\/\/ror.org\/00p7p3302","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a comparative study of two artificial intelligence approaches\u2014graph convolutional networks (GCNs) and the YOLO object detection algorithm\u2014for analyzing human fall events using infrared imaging. From the AI perspective, the study introduces a GCN model that achieves over 99% classification accuracy by modeling 2D and 3D skeletal data as graph structures and evaluates the real-time detection capabilities of YOLOv8 on infrared video frames. On the engineering side, the research addresses practical challenges in elderly care and healthcare monitoring systems by demonstrating how these AI methods can accurately detect and classify fall directions under infrared conditions. The results highlight each model\u2019s strengths and propose a hybrid framework combining YOLO\u2019s spatial localization with GCN\u2019s motion-pattern analysis for future real-world applications.<\/jats:p>","DOI":"10.3390\/s26092794","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T10:47:28Z","timestamp":1777546048000},"page":"2794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Human Fall Detection with Infrared Imaging: A Comparison of Graph Convolutional Networks and YOLO"],"prefix":"10.3390","volume":"26","author":[{"given":"Karol","family":"Perli\u0144ski","sequence":"first","affiliation":[{"name":"Institute of Automatic Control and Robotics, Poznan University of Technology, 61-131 Pozna\u0144, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Artur","family":"Falty\u0144ski","sequence":"additional","affiliation":[{"name":"Institute of Automatic Control and Robotics, Poznan University of Technology, 61-131 Pozna\u0144, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2576-7625","authenticated-orcid":false,"given":"Aleksandra","family":"\u015awietlicka","sequence":"additional","affiliation":[{"name":"Institute of Automatic Control and Robotics, Poznan University of Technology, 61-131 Pozna\u0144, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"124","DOI":"10.5334\/paah.459","article-title":"Ability of a New Fear of Falling Scale in Assessing Fall Risk and Its Correlation with Functional Balance in Community-Dwelling Older Adults","volume":"9","author":"Poncumhak","year":"2025","journal-title":"Phys. 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