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Time Series Data Augmentation and Dropout Roles in Deep Learning Applied to Fall Detection

Author:
García González, EnolUniovi authority; Villar Flecha, José RamónUniovi authority; Cal Marín, Enrique Antonio de laUniovi authority
Publication date:
2021
Editorial:

springer

Publisher version:
http://dx.doi.org/10.1007/978-3-030-57802-2_54
Citación:
González E.G., Villar J.R., de la Cal E. (2021) Time Series Data Augmentation and Dropout Roles in Deep Learning Applied to Fall Detection. En: Herrero Á., Cambra C., Urda D., Sedano J., Quintián H., Corchado E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020.
Serie:

Advances in Intelligent Systems and Computing; 1268

Descripción física:
p. 563-570
URI:
http://hdl.handle.net/10651/57976
ISBN:
978-3-030-57801-5
DOI:
10.1007/978-3-030-57802-2_54
Patrocinado por:

This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R and by the Grant FCGRUPIN-IDI/2018/000226 project from the Asturias Regional Government.

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