Publication
IoT-Based Human Fall Detection System
| dc.contributor.author | Ribeiro, Osvaldo | |
| dc.contributor.author | Gomes, Luis | |
| dc.contributor.author | Vale, Zita | |
| dc.date.accessioned | 2023-02-01T10:34:45Z | |
| dc.date.available | 2023-02-01T10:34:45Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Human falls are an issue that especially affects elderly people, resulting in permanent disabilities or even in the person’s 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’s 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. | pt_PT |
| dc.description.sponsorship | The present work has received funding from the European Regional Development Fund (FEDER) through the Northern Regional Operational Program, under the PORTUGAL 2020 Partnership Agreement and the terms of the NORTE-45-2020-75 call—Support System for Scientific and Technological Research—“Structured R&D&I Projects”—Horizon Europe, within project RETINA (NORTE 01-0145-FEDER-000062). | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.3390/electronics11040592 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/22048 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.relation | NORTE-45-2020-75 | pt_PT |
| dc.relation | NORTE 01-0145-FEDER-000062 | pt_PT |
| dc.relation.publisherversion | https://www.mdpi.com/2079-9292/11/4/592 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Artificial neural network | pt_PT |
| dc.subject | Fall detection systems | pt_PT |
| dc.subject | Internet of things devices | pt_PT |
| dc.subject | Morlet wavelet | pt_PT |
| dc.title | IoT-Based Human Fall Detection System | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 4 | pt_PT |
| oaire.citation.startPage | 592 | pt_PT |
| oaire.citation.title | Electronics | pt_PT |
| oaire.citation.volume | 11 | pt_PT |
| person.familyName | Vale | |
| person.givenName | Zita | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | 6F19-CB63-C8A8 | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.orcid | 0000-0002-8597-3383 | |
| person.identifier.orcid | 0000-0002-4560-9544 | |
| person.identifier.rid | A-5824-2012 | |
| person.identifier.scopus-author-id | 7004115775 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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