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Presentation

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Lower-Limb Falling Detection System Using Gated Recurrent Neural Networks

Author/sLuna Perejón, Francisco  
Muñoz Saavedra, Luis            
Civit Masot, Javier              
Montes-Sánchez, Juan Manuel  
Domínguez Morales, Manuel Jesús                
Civit Balcells, Antón                
DepartmentUniversidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores
Date2020-03-22
Published in eTELEMED 2020: The Twelfth International Conference on eHealth, Telemedicine, and Social Medicine (2020), pp. 1-4.
ISBN/ISSN978-1-61208-763-4
2308-4359
AbstractAccidental falls are one of the most common causes of premature disability and mortality related to unnatural causes. This affects mainly the elderly population. With the current aging of the population, the rate of ...
Accidental falls are one of the most common causes of premature disability and mortality related to unnatural causes. This affects mainly the elderly population. With the current aging of the population, the rate of accidental falls increases. Computer systems for gait analysis and fast assistance in ubiquitous environments can be effective tools to prevent these accidents. In this article we present the advances in the creation of an intelligent device for detecting falls and risk situations based on accelerometer signals registered on the user’s ankle. The proposed method makes use of Deep Learning techniques, specifically Gated Recurrent Neural Networks. The results show that the proposed model is a viable alternative to detect falls and fall risk, which can be implemented in low performance devices for greater autonomy, lower cost and comfortable portability. These results open the possibility of combining fall detection with a biomechanical analysis system to identify gait deficiencies and their relation with falls.
CitationLuna Perejón, F., Muñoz Saavedra, L., Civit Masot, J., Montes-Sánchez, J.M., Domínguez Morales, M.J. y Civit Balcells, A. (2020). Lower-Limb Falling Detection System Using Gated Recurrent Neural Networks. En eTELEMED 2020: The Twelfth International Conference on eHealth, Telemedicine, and Social Medicine (1-4), Valencia: IARIA XPS Press.
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Logo Handlehttps://hdl.handle.net/11441/111302
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