Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/71659
Título: | Assistive Smart Cane (ASCane) for fall detection: first advances |
Autor(es): | Mouta, Pedro Ribeiro, Nuno Miguel Ferrete Santos, Cristina Moreira, Rui |
Palavras-chave: | Activities of daily living Fall detection IMU Machine learning |
Data: | 2020 |
Editora: | Springer, Cham |
Revista: | IFMBE Proceedings |
Citação: | Mouta P., Ribeiro N.F., Santos C.P., Moreira R. (2020) Assistive Smart Cane (ASCane) for Fall Detection: First Advances. In: Henriques J., Neves N., de Carvalho P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_204 |
Resumo(s): | The development of fall detection systems with the capability of real-time monitoring is necessary considering that a large amount of people die and suffer severe consequences from falls. Due to their advantages, daily life accessories can be a solution to embed fall-related systems, and canes are no exception. In this paper, it is presented a cane with fall detection abilities. The ASCane is instrumented with an inertial sensor which data will be tested with three different fixed multi-threshold fall detection algorithms, one dynamic multi-threshold and machine learning methods from the literature. They were tested and modified to account the use of a cane. The best performance resulted in a sensitivity and specificity of 96.90% and 98.98%, respectively. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/71659 |
ISBN: | 978-3-030-31634-1 |
e-ISBN: | 978-3-030-31635-8 |
DOI: | 10.1007/978-3-030-31635-8_204 |
ISSN: | 1680-0737 |
Versão da editora: | https://link.springer.com/chapter/10.1007%2F978-3-030-31635-8_204 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | DEI - Artigos em atas de congressos internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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MEDICON2019_paper_141.pdf | 2,91 MB | Adobe PDF | Ver/Abrir |