Utilize este identificador para referenciar este registo: http://hdl.handle.net/10198/10814
Título: Fall detection systems to be used by elderly people
Autor: Igrejas, Getúlio
Amaral, J.S.
Rodrigues, P.J.S.
Palavras-chave: Fall detector
Neural networks
Machine learning
Elderly people
Data: 2013
Editora: IGI Global
Citação: Igrejas, Getúlio; Amaral, J.S.; Rodrigues, P.J.S. (2013) - Fall detection systems to be used by elderly people. In Cruz-Cunha, Maria Manuela [et al.] Handbook of Research on ICTs for Human-Centered Healthcare and Social Care Services. IGI Global. p.449-473. ISBN 9781466639867
Resumo: Statistics show that, each year, falls affect tens of millions of elderly people throughout the world. Falls are the leading cause of injury deaths and injury-related hospitalization among people over 65 years old. A system able to automatically detect falls could be an important tool from a social point of view, as it would contribute to the prompt assistance of these emergency cases. Currently, many researchers are interested in the development of fall detection systems. This chapter presents several approaches suggested so far, with special attention to the different strategies and technologies applied. Other sen- sor technologies that could be applied to this field are also referred. Additionally, a new fall detection system based on a machine learning paradigm, using neural networks, is suggested. The system was trained using fall and non-fall examples. Although the test set has included some particular examples, problematic for detection of the correspondent motion, the obtained results present good specificity (88.9%) and sensitivity (93.9%) rates.
Peer review: yes
URI: http://hdl.handle.net/10198/10814
ISBN: 9781466639867
Aparece nas colecções:CIMO - Capítulos de Livros

Ficheiros deste registo:
Ficheiro Descrição TamanhoFormato 
Fall.pdf808,21 kBAdobe PDFVer/Abrir    Acesso Restrito. Solicitar cópia ao autor!

FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Degois 

Todos os registos no repositório estão protegidos por leis de copyright, com todos os direitos reservados.