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Assistive Smart Cane (ASCane) for fall detection: first advances

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Detalhes bibliográficos
Resumo: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.
Autores principais:Mouta, Pedro
Outros Autores:Ribeiro, Nuno Miguel Ferrete; Santos, Cristina; Moreira, Rui
Assunto:Activities of daily living Fall detection IMU Machine learning Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Ano:2020
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso aberto
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho
Descrição
Resumo: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.