Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/32082
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Castillo, José Carlos | por |
dc.contributor.author | Carneiro, Davide Rua | por |
dc.contributor.author | Serrano-Cuerda, Juan | por |
dc.contributor.author | Novais, Paulo | por |
dc.contributor.author | Fernández-Caballero, Antonio | por |
dc.contributor.author | Neves, José | por |
dc.date.accessioned | 2014-12-16T15:36:05Z | - |
dc.date.available | 2014-12-16T15:36:05Z | - |
dc.date.issued | 2014 | - |
dc.identifier.issn | 0020-7721 | - |
dc.identifier.uri | https://hdl.handle.net/1822/32082 | - |
dc.description | "Special issue : Intelligent multisensory systems in support of information society" | por |
dc.description.abstract | The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment. | por |
dc.description.sponsorship | This work is funded by National Funds through the FCT-Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst- OE/EEI/UI0752/2011. The work of Davide Carneiro is also supported by a doctoral grant by FCT (SFRH/BD/64890/2009). This work is also partially supported by the Spanish Ministerio de Economía y Competitividad / FEDER under project TIN2010-20845-C03-01. | por |
dc.language.iso | eng | por |
dc.publisher | Taylor and Francis | por |
dc.rights | openAccess | por |
dc.subject | Activity classification | por |
dc.subject | Fall detection | por |
dc.subject | Behavioural analysis | por |
dc.title | A multi-modal approach for activity classification and fall detection | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | http://www.tandfonline.com/doi/abs/10.1080/00207721.2013.784372#.VITkZzGsVzU | por |
sdum.publicationstatus | published | por |
oaire.citationStartPage | 810 | por |
oaire.citationEndPage | 824 | por |
oaire.citationIssue | 4 | por |
oaire.citationTitle | International journal of systems science | por |
oaire.citationVolume | 45 | por |
dc.identifier.doi | 10.1080/00207721.2013.784372 | por |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | International journal of systems science | por |
Aparece nas coleções: | CCTC - Artigos em revistas internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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IJSSv5.0.pdf | 1,27 MB | Adobe PDF | Ver/Abrir |