Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/32082

TitleA multi-modal approach for activity classification and fall detection
Author(s)Castillo, José Carlos
Carneiro, Davide Rua
Serrano-Cuerda, Juan
Novais, Paulo
Fernández-Caballero, Antonio
Neves, José
KeywordsActivity classification
Fall detection
Behavioural analysis
Issue date2014
PublisherTaylor & Francis
JournalInternational journal of systems science
Abstract(s)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.
TypeArticle
Description"Special issue : Intelligent multisensory systems in support of information society"
URIhttp://hdl.handle.net/1822/32082
DOI10.1080/00207721.2013.784372
ISSN0020-7721
Publisher versionhttp://www.tandfonline.com/doi/abs/10.1080/00207721.2013.784372#.VITkZzGsVzU
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CCTC - Artigos em revistas internacionais

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