Mostrar el registro sencillo del ítem
Improving Wearable-based Fall Detection with unsupervised learning
dc.contributor.author | Fáñez, Mirko | |
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 | 2020-09-26T14:42:34Z | |
dc.date.available | 2020-09-26T14:42:34Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Logic Journal of the IGPL | |
dc.identifier.citation | Logic journal of the IGPL, 30(2), p. 314-325 (2022); doi:10.1093/jigpal/jzaa064 | |
dc.identifier.issn | 1368-9894 | |
dc.identifier.uri | http://hdl.handle.net/10651/56920 | |
dc.description.abstract | Fall detection (FD) is a challenging task that has received the attention of the research community in the recent years. This study focuses on FD using data gathered from wearable devices with tri-axial accelerometers (3DACC), developing a solution centered in elderly people living autonomously. This research includes three different ways to improve a FD method: i) an analysis of the event detection stage, comparing several alternatives, ii) an evaluation of features to extract for each detected event and, iii) an appraisal of up to 6 different clustering scenarios to split the samples in subsets that might enhance the classification. For each clustering scenario, a specific classification stage is defined. The experimentation includes publicly available simulated fall data sets. Results show the guidelines for defining a more robust and efficient FD method for on-wrist 3DACC wearable devices. | spa |
dc.description.sponsorship | Spanish Ministry of Science and Innovation [MINECO-TIN2017-84804-R]; Asturias Regional Government [FCGRUPIN-IDI/2018/000226]; Instituto para la Competitividad Empresarial de Castilla y León [CCTT2/18/BU/0002] | |
dc.format.extent | p. 314-325 | |
dc.language.iso | eng | spa |
dc.relation.ispartof | Logic Journal of the IGPL | spa |
dc.rights | © The authors 2020. Published by Oxford University Press | |
dc.subject | Fall detection | spa |
dc.subject | Unsupervised learning | spa |
dc.subject | Clustering | spa |
dc.subject | One-class classifiers | spa |
dc.title | Improving Wearable-based Fall Detection with unsupervised learning | spa |
dc.type | journal article | spa |
dc.identifier.doi | 10.1093/jigpal/jzaa064 | |
dc.relation.projectID | FC-GRUPIN-IDI/2018/000226 | spa |
dc.relation.projectID | MINECO-TIN2017-84804-R | |
dc.relation.publisherversion | http://dx.doi.org/10.1093/jigpal/jzaa064 | |
dc.rights.accessRights | open access | spa |
dc.type.hasVersion | AM |
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
-
Artículos [34664]
-
Ingeniería Eléctrica, Electrónica, de Comunicaciones y de Sistemas [980]
-
Investigaciones y Documentos OpenAIRE [7577]
Publicaciones resultado de proyectos financiados con fondos públicos