{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T19:42:40Z","timestamp":1778096560036,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T00:00:00Z","timestamp":1611964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors\u2019 sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection.<\/jats:p>","DOI":"10.3390\/s21030938","type":"journal-article","created":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T21:15:51Z","timestamp":1612041351000},"page":"938","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6704-9031","authenticated-orcid":false,"given":"Nicolas","family":"Zurbuchen","sequence":"first","affiliation":[{"name":"Institute of Complex Systems (iCoSys), School of Engineering and Architecture of Fribourg Switzerland, HES-SO University of Applied Sciences and Arts Western Switzerland, 1700 Fribourg, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1684-1539","authenticated-orcid":false,"given":"Adriana","family":"Wilde","sequence":"additional","affiliation":[{"name":"Centre for Health Technologies (CHT), School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK"},{"name":"Department of Digital Technologies, Faculty of Business, Law and Digital Technologies, University of Winchester, Winchester SO22 4NR, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6238-3225","authenticated-orcid":false,"given":"Pascal","family":"Bruegger","sequence":"additional","affiliation":[{"name":"Institute of Complex Systems (iCoSys), School of Engineering and Architecture of Fribourg Switzerland, HES-SO University of Applied Sciences and Arts Western Switzerland, 1700 Fribourg, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"ii37","DOI":"10.1093\/ageing\/afl084","article-title":"Falls in older people: Epidemiology, risk factors and strategies for prevention","volume":"35","author":"Rubenstein","year":"2006","journal-title":"Age Ageing"},{"key":"ref_2","unstructured":"World Health Organization (2008). 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