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dc.contributor.authorBanos, Oresti
dc.contributor.authorGálvez, Juan Manuel
dc.contributor.authorDamas, Miguel
dc.contributor.authorGuillén, Alberto
dc.contributor.authorHerrera, Luis Javier
dc.contributor.authorPomares, Héctor
dc.contributor.authorRojas, Ignacio
dc.contributor.authorVillalonga, Claudia (1)
dc.date2019
dc.date.accessioned2020-09-14T14:21:32Z
dc.date.available2020-09-14T14:21:32Z
dc.identifier.citationBanos O. et al. (2019) Improving Wearable Activity Recognition via Fusion of Multiple Equally-Sized Data Subwindows. In: Rojas I., Joya G., Catala A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science, vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_30es_ES
dc.identifier.isbn9783030205201
dc.identifier.issn0302-9743
dc.identifier.urihttps://reunir.unir.net/handle/123456789/10561
dc.descriptionPonencia de la conferencia "15th International Work-Conference on Artificial Neural Networks, IWANN 2019; Gran Canaria; Spain; 12 June 2019 through 14 June 2019"es_ES
dc.description.abstractThe automatic recognition of physical activities typically involves various signal processing and machine learning steps used to transform raw sensor data into activity labels. One crucial step has to do with the segmentation or windowing of the sensor data stream, as it has clear implications on the eventual accuracy level of the activity recogniser. While prior studies have proposed specific window sizes to generally achieve good recognition results, in this work we explore the potential of fusing multiple equally-sized subwindows to improve such recognition capabilities. We tested our approach for eight different subwindow sizes on a widely-used activity recognition dataset. The results show that the recognition performance can be increased up to 15% when using the fusion of equally-sized subwindows compared to using a classical single window.es_ES
dc.language.isoenges_ES
dc.publisherLecture Notes in Computer Sciencees_ES
dc.relation.ispartofseries;vol. 11506
dc.relation.urihttps://link.springer.com/chapter/10.1007%2F978-3-030-20521-8_30es_ES
dc.rightsrestrictedAccesses_ES
dc.subjectactivity recognitiones_ES
dc.subjectdata fusiones_ES
dc.subjectdata windowes_ES
dc.subjectsegmentationes_ES
dc.subjectwearable sensorses_ES
dc.subjectScopus(2)es_ES
dc.subjectWOS(2)es_ES
dc.titleImproving Wearable Activity Recognition via Fusion of Multiple Equally-Sized Data Subwindowses_ES
dc.typeconferenceObjectes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1007/978-3-030-20521-8_30


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