Feature Clustering to Improve Fall Detection: A Preliminary Study

dc.contributor.author	Fáñez, M.
dc.contributor.author	Villar Flecha, José Ramón
dc.contributor.author	Cal Marín, Enrique Antonio de la
dc.contributor.author	González Suárez, Víctor Manuel
dc.contributor.author	Sedano, Javier
dc.date.accessioned	2019-11-14T10:21:52Z
dc.date.available	2019-11-14T10:21:52Z
dc.date.issued	2020
dc.identifier.isbn	9783030200541
dc.identifier.issn	2194-5357
dc.identifier.uri	http://hdl.handle.net/10651/53097
dc.description	International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO (14th. 2019. Seville, Spain)
dc.description.sponsorship	This research has been funded by the Spanish Ministry of Science and Innovation, under project MINECO-TIN2017-84804-R.
dc.format.extent	p. 219-228
dc.language.iso	eng
dc.relation.ispartof	14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019)
dc.relation.ispartofseries	Advances in Intelligent Systems and Computing
dc.rights	© Springer Nature Switzerland AG 2020
dc.source	Scopus
dc.source.uri	https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85065908288&doi=10.1007%2f978-3-030-20055-8_21&partnerID=40&md5=873b97ba6a0e9460f225b0e6eb4cf102
dc.title	Feature Clustering to Improve Fall Detection: A Preliminary Study
dc.type	conference output	spa
dc.identifier.doi	10.1007/978-3-030-20055-8_21
dc.relation.projectID	MINECO-TIN2017-84804-R
dc.relation.publisherversion	http://dx.doi.org/10.1007/978-3-030-20055-8_21

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