{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:30:39Z","timestamp":1777735839625,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T00:00:00Z","timestamp":1644883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund (FEDER) through the Northern Regional Operational Program","award":["NORTE 01-0145-FEDER-000062"],"award-info":[{"award-number":["NORTE 01-0145-FEDER-000062"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Electronics"],"abstract":"<jats:p>Human falls are an issue that especially affects elderly people, resulting in permanent disabilities or even in the person\u2019s death. Preventing human falls is a social desire, but it is almost impossible to achieve because it is not possible to ensure full prevention. A possible solution is the detection of human falls in near real-time so that help can quickly be provided. This has the potential to greatly reduce the severity of the fall in long-term health consequences. This work proposes a solution based on the internet of things devices installed in people\u2019s homes. The proposed non-wearable solution is non-intrusive and can be deployed not only in homes but also in hospitals, rehabilitation facilities, and elderly homes. The solution uses a three-layered computation architecture composed of edge, fog, and cloud. A mathematical model using the Morlet wavelet and an artificial intelligence model using artificial neural networks are used for human fall classification; both approaches are compared. The results showed that the combination of both models is possible and brings benefits to the system, achieving an accuracy of 92.5% without false negatives.<\/jats:p>","DOI":"10.3390\/electronics11040592","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T22:43:22Z","timestamp":1644965002000},"page":"592","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["IoT-Based Human Fall Detection System"],"prefix":"10.3390","volume":"11","author":[{"given":"Osvaldo","family":"Ribeiro","sequence":"first","affiliation":[{"name":"GECAD\u2014Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto, P-4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8597-3383","authenticated-orcid":false,"given":"Luis","family":"Gomes","sequence":"additional","affiliation":[{"name":"GECAD\u2014Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto, P-4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4560-9544","authenticated-orcid":false,"given":"Zita","family":"Vale","sequence":"additional","affiliation":[{"name":"GECAD\u2014Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto, P-4200-072 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"ref_1","unstructured":"(2021, November 09). 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