dc.creator | Prado-Velasco, Manuel | es |
dc.creator | Ortiz Marín, Rafael | es |
dc.creator | Río Cidoncha, María Gloria del | es |
dc.date.accessioned | 2016-12-30T13:02:29Z | |
dc.date.available | 2016-12-30T13:02:29Z | |
dc.date.issued | 2013-10-10 | |
dc.identifier.citation | Prado-Velasco, M., Ortiz Marín, R. y Rio Cidoncha, G.d. (2013). Detection of human impacts by an adaptive energy-based anisotropic algorithm. International Journal of Environmental Research and Public Health, 10 (10), 4767-4789. | |
dc.identifier.issn | 1660-4601 | es |
dc.identifier.uri | http://hdl.handle.net/11441/51393 | |
dc.description.abstract | Boosted by health consequences and the cost of falls in the elderly, this work
develops and tests a novel algorithm and methodology to detect human impacts that will
act as triggers of a two-layer fall monitor. The two main requirements demanded by
socio-healthcare providers—unobtrusiveness and reliability—defined the objectives of the
research. We have demonstrated that a very agile, adaptive, and energy-based anisotropic
algorithm can provide 100% sensitivity and 78% specificity, in the task of detecting
impacts under demanding laboratory conditions. The algorithm works together with an
unsupervised real-time learning technique that addresses the adaptive capability, and this is
also presented. The work demonstrates the robustness and reliability of our new algorithm,
which will be the basis of a smart falling monitor. This is shown in this work to underline
the relevance of the results. | es |
dc.description.sponsorship | Ministerio de Industria y Comercio TSI-020100-2008-64 (ISIS) | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | International Journal of Environmental Research and Public Health, 10 (10), 4767-4789. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Adaptive algorithm | es |
dc.subject | Energy-based impact detection | es |
dc.subject | Unsupervised learning technique | es |
dc.subject | Telehealth services | es |
dc.subject | Distributed signal processing | es |
dc.subject | Smart sensor | es |
dc.title | Detection of human impacts by an adaptive energy-based anisotropic algorithm | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Gráfica | es |
dc.relation.publisherversion | http://dx.doi.org/10.3390/ijerph10104767 | es |
dc.identifier.doi | 10.3390/ijerph10104767 | es |
dc.contributor.group | Universidad de Sevilla. TIC214: Modelado Multiescala y Tecnologias Emergentes en Bioingenieria | es |
idus.format.extent | 23 p. | es |
dc.journaltitle | International Journal of Environmental Research and Public Health | es |
dc.publication.volumen | 10 | es |
dc.publication.issue | 10 | es |
dc.publication.initialPage | 4767 | es |
dc.publication.endPage | 4789 | es |
dc.contributor.funder | Ministerio de Industria y Comercio. España | |
dc.contributor.funder | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) | |