{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:28:46Z","timestamp":1769117326080,"version":"3.49.0"},"reference-count":18,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2014,2,11]],"date-time":"2014-02-11T00:00:00Z","timestamp":1392076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect\u00ae depth sensor, in an \u201con-ceiling\u201d configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he\/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios.<\/jats:p>","DOI":"10.3390\/s140202756","type":"journal-article","created":{"date-parts":[[2014,2,11]],"date-time":"2014-02-11T11:10:42Z","timestamp":1392117042000},"page":"2756-2775","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":169,"title":["A Depth-Based Fall Detection System Using a Kinect\u00ae Sensor"],"prefix":"10.3390","volume":"14","author":[{"given":"Samuele","family":"Gasparrini","sequence":"first","affiliation":[{"name":"Dipartimento di Ingegneria dell'Informazione, Universit\u00e0 Politecnica delle Marche,  Via Brecce Bianche 12, Ancona 60131, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enea","family":"Cippitelli","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria dell'Informazione, Universit\u00e0 Politecnica delle Marche,  Via Brecce Bianche 12, Ancona 60131, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7323-4030","authenticated-orcid":false,"given":"Susanna","family":"Spinsante","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria dell'Informazione, Universit\u00e0 Politecnica delle Marche,  Via Brecce Bianche 12, Ancona 60131, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6852-8483","authenticated-orcid":false,"given":"Ennio","family":"Gambi","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria dell'Informazione, Universit\u00e0 Politecnica delle Marche,  Via Brecce Bianche 12, Ancona 60131, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Raul, I., Medrano, C., and Plaza, I. (2013). Challenges, issues and trends in fall detection systems. Biomed. Eng. Online, 12.","DOI":"10.1186\/1475-925X-12-66"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/TNSRE.2007.916282","article-title":"Portable preimpact fall detector with inertial sensors","volume":"16","author":"Wu","year":"2008","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_3","unstructured":"Charif, H.N., and Mckenna, S. (2004, January 23\u201326). Activity Summarization and Fall Detection in a Supportive Home Environment. Cambridge, UK."},{"key":"ref_4","unstructured":"MESA Imaging. Available online: http:\/\/www.mesa-imaging.ch\/."},{"key":"ref_5","unstructured":"Kinect for Windows. Available online: http:\/\/www.microsoft.com\/en-us\/kinectforwindows\/."},{"key":"ref_6","unstructured":"NITE 2. Available online: http:\/\/www.openni.org\/files\/nite\/."},{"key":"ref_7","unstructured":"Skeletal Tracking. Available online: http:\/\/msdn.microsoft.com\/en-us\/library\/hh973074.aspx."},{"key":"ref_8","unstructured":"Bingbing, N., Gang, W., and Moulin, P. (2011, January 6\u201313). RGBD-HuDaAct: A Color-Depth Video Database for Human Daily Activity Recognition. Barcelona, Spain."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wichert, R., and Eberhardt, B. (2012). Ambient Assisted Living, Springer.","DOI":"10.1007\/978-3-642-27491-6"},{"key":"ref_10","unstructured":"Mastorakis, G., and Makris, D. Fall Detection System Using Kinect's Infrared Sensor. Available online: http:\/\/link.springer.com\/article\/10.1007%2Fs11554-012-0246-9."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1049\/iet-cvi.2011.0140","article-title":"Who is who at different cameras: people re-identification using depth cameras","volume":"6","author":"Albiol","year":"2012","journal-title":"IET Comput. 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[2nd ed.].","DOI":"10.1007\/978-3-662-05088-0"},{"key":"ref_16","unstructured":"Gonzalez, R.C., and Woods, R.E. (2006). Digital Image Processing, Prentice-Hall Inc.. [3nd ed.]."},{"key":"ref_17","unstructured":"NASA (1978). Anthropometric Source Book in A Handbook of Anthropometric Data, Staff of Anthropology Research Project."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Webb, J., and Ashley, J. (2012). Beginning Kinect Programming with the Microsoft Kinect. SDK, Apress Media LLC.","DOI":"10.1007\/978-1-4302-4105-8"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/2\/2756\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:08:05Z","timestamp":1760216885000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/2\/2756"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,2,11]]},"references-count":18,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2014,2]]}},"alternative-id":["s140202756"],"URL":"https:\/\/doi.org\/10.3390\/s140202756","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,2,11]]}}}