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Transfer learning and information retrieval applied to fall detection
dc.contributor.author | Fáñez Kertelj, Mirko | |
dc.contributor.author | Villar Flecha, José Ramón | |
dc.contributor.author | Cal Marín, Enrique Antonio de la | |
dc.contributor.author | Sedano, Javier | |
dc.contributor.author | González Suárez, Víctor Manuel | |
dc.date.accessioned | 2020-09-26T15:03:26Z | |
dc.date.available | 2020-09-26T15:03:26Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Expert Systems (2020); doi:10.1111/exsy.12522 | |
dc.identifier.issn | 1468-0394 | |
dc.identifier.uri | http://hdl.handle.net/10651/56922 | |
dc.description.abstract | Detecting falls in the elderly population is a very important issue that is related with the time of recovery. This study focuses on using wearable smart watches to monitor the movements of the user in order to detect patterns that might be related to fall events. The proposed solution explores Symbolic Aggregate approXimation (SAX) Time Series representation, together with two information retrieval techniques enriched with transfer learning (TL). The solution is user centred; that is, a model is developed for each specific user. Basically, the fall detection approach makes use of a finite-state machine to detect peaks; the time series window embedding these peaks are represented using SAX. Assuming the data from the public fall detection data sets are valid, a dictionary is prepared using the most relevant words. This dic- tionary is then introduced as previous knowledge to an online learning classifier that is trained with normal activities of daily living. The two classifiers are evaluated and compared with two classical approaches. Before this comparison, two clustering approaches are studied to produce the bag of relevant words. A complete experimen- tation is included, which makes use of several publicly available data sets and also with a data set developed by the research group. Comparisons are performed for all the data sets, showing how the TL stage empowers the classifier. The results show that this solution produces high detection rates and at the same time performed simi- larly for all the individuals tested. Furthermore, the positive effects of TL in this con- text are clearly remarked. | spa |
dc.description.sponsorship | 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 (Gobierno del Principado de Asturias). | |
dc.format.extent | p. 1-28 | spa |
dc.language.iso | eng | spa |
dc.relation.ispartof | Expert Systems | spa |
dc.rights | © 2020 John Wiley & Sons, Ltd | |
dc.subject | Fall detection | spa |
dc.subject | Machine Learning | spa |
dc.subject | Time Series | spa |
dc.subject | Transfer Learning | spa |
dc.title | Transfer learning and information retrieval applied to fall detection | spa |
dc.type | journal article | spa |
dc.identifier.doi | 10.1111/exsy.12522 | |
dc.relation.projectID | FCGRUPIN-IDI/2018/000226 | spa |
dc.relation.projectID | MINECO-TIN2017-84804-R | |
dc.relation.publisherversion | http://dx.doi.org/10.1111/exsy.12522 | |
dc.rights.accessRights | open access | spa |
dc.type.hasVersion | AM |
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