Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/32082
Título: | A multi-modal approach for activity classification and fall detection |
Autor(es): | Castillo, José Carlos Carneiro, Davide Rua Serrano-Cuerda, Juan Novais, Paulo Fernández-Caballero, Antonio Neves, José |
Palavras-chave: | Activity classification Fall detection Behavioural analysis |
Data: | 2014 |
Editora: | Taylor and Francis |
Revista: | International journal of systems science |
Resumo(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. |
Tipo: | Artigo |
Descrição: | "Special issue : Intelligent multisensory systems in support of information society" |
URI: | https://hdl.handle.net/1822/32082 |
DOI: | 10.1080/00207721.2013.784372 |
ISSN: | 0020-7721 |
Versão da editora: | http://www.tandfonline.com/doi/abs/10.1080/00207721.2013.784372#.VITkZzGsVzU |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
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 |