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Highly-efficient fog-based deep learning AAL fall detection system

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Highly-efficient fog-based deep learning AAL fall detection system

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Sarabia-Jácome, D.; Usach, R.; Palau Salvador, CE.; Esteve Domingo, M. (2020). Highly-efficient fog-based deep learning AAL fall detection system. Internet of Things. 11:1-19. https://doi.org/10.1016/j.iot.2020.100185

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/169535

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Title: Highly-efficient fog-based deep learning AAL fall detection system
Author: Sarabia-Jácome, David Usach, Regel Palau Salvador, Carlos Enrique Esteve Domingo, Manuel
UPV Unit: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Issued date:
Abstract:
[EN] Falls is one of most concerning accidents in aged population due to its high frequency and serious repercussion; thus, quick assistance is critical to avoid serious health consequences. There are several Ambient ...[+]
Subjects: IoT , Big data , Fog computing , Cloud computing , Deep learning , AAL , Health , AHA, Fall
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Internet of Things. (eissn: 2542-6605 )
DOI: 10.1016/j.iot.2020.100185
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.iot.2020.100185
Project ID:
info:eu-repo/grantAgreement/EC/H2020/732679/EU
Thanks:
This research was supported by the Ecuadorian Government through the Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT) and has received funding from the European Union's Horizon 2020 research ...[+]
Type: Artículo

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