{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T02:19:58Z","timestamp":1780366798160,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,13]],"date-time":"2020-11-13T00:00:00Z","timestamp":1605225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["288940"],"award-info":[{"award-number":["288940"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We analyzed the acceleration signals recorded by an inertial sensor on the lower back during 143 real-world falls (the most extensive collection to date) from the FARSEEING repository. Such data were obtained from continuous real-world monitoring of subjects with a moderate-to-high risk of falling. We designed and tested fall detection algorithms using features inspired by a multiphase fall model and a machine learning approach. The obtained results suggest that algorithms can learn effectively from features extracted from a multiphase fall model, consistently overperforming more conventional features. The most promising method (support vector machines and features from the multiphase fall model) obtained a sensitivity higher than 80%, a false alarm rate per hour of 0.56, and an F-measure of 64.6%. The reported results and methodologies represent an advancement of knowledge on real-world fall detection and suggest useful metrics for characterizing fall detection systems for real-world use.<\/jats:p>","DOI":"10.3390\/s20226479","type":"journal-article","created":{"date-parts":[[2020,11,13]],"date-time":"2020-11-13T08:44:02Z","timestamp":1605257042000},"page":"6479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4758-662X","authenticated-orcid":false,"given":"Luca","family":"Palmerini","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic, and Information Engineering \u201cGuglielmo Marconi\u201d, University of Bologna, 40136 Bologna, Italy"},{"name":"Health Sciences and Technologies\u2014Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, 40126 Bologna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5987-447X","authenticated-orcid":false,"given":"Jochen","family":"Klenk","sequence":"additional","affiliation":[{"name":"Department of Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany"},{"name":"Institute of Epidemiology and Medical Biometry, Ulm University, 89081 Ulm, Germany"},{"name":"Study Centre Stuttgart, IB University of Applied Health and Social Sciences, 70178 Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clemens","family":"Becker","sequence":"additional","affiliation":[{"name":"Department of Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2318-4370","authenticated-orcid":false,"given":"Lorenzo","family":"Chiari","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic, and Information Engineering \u201cGuglielmo Marconi\u201d, University of Bologna, 40136 Bologna, Italy"},{"name":"Health Sciences and Technologies\u2014Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, 40126 Bologna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/1475-925X-12-66","article-title":"Challenges, issues and trends in fall detection systems","volume":"12","author":"Igual","year":"2013","journal-title":"Biomed. 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