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Deep learning models for real-life human activity recognition from smartphone sensor data
dc.contributor.author | García-González, Daniel | |
dc.contributor.author | Rivero, Daniel | |
dc.contributor.author | Fernández-Blanco, Enrique | |
dc.contributor.author | Rodríguez Luaces, Miguel | |
dc.date.accessioned | 2024-07-01T17:50:38Z | |
dc.date.available | 2024-07-01T17:50:38Z | |
dc.date.issued | 2023-12 | |
dc.identifier.citation | Garcia-Gonzalez, D., Rivero, D., Fernandez-Blanco, E., & Luaces, M. R. (2023). Deep learning models for real-life human activity recognition from smartphone sensor data. Internet of Things, 24, 100925. https://doi.org/10.1016/j.iot.2023.100925 | es_ES |
dc.identifier.issn | 2542-6605 | |
dc.identifier.uri | http://hdl.handle.net/2183/37606 | |
dc.description | Data availability The original complete dataset, as well as some of the scripts used to preprocess its data, can be found online at http://lbd.udc.es/research/real-life-HAR-dataset . Likewise, these have also been uploaded to Mendeley Data (https://data.mendeley.com/datasets/3xm88g6m6d/2). In addition, the code used for the entire data preparation and experimentation in this work is available online at http://gitlab.lbd.org.es/dgarcia/deep-learning-models-har | es_ES |
dc.description.abstract | [Abstract]: Nowadays, the field of human activity recognition (HAR) is a remarkably hot topic within the scientific community. Given the low cost, ease of use and high accuracy of the sensors from different wearable devices and smartphones, more and more researchers are opting to do their bit in this area. However, until very recently, all the work carried out in this field was done in laboratory conditions, with very few similarities with our daily lives. This paper will focus on this new trend of integrating all the knowledge acquired so far into a real-life environment. Thus, a dataset already published following this philosophy was used. In this way, this work aims to be able to identify the different actions studied there. In order to perform this classification, this paper explores new designs and architectures for models inspired by the ones which have yielded the best results in the literature. More specifically, different configurations of Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) have been tested, but on real-life conditions instead of laboratory ones. It is worth mentioning that the hybrid models formed from these techniques yielded the best results, with a peak accuracy of 94.80% on the dataset used. | es_ES |
dc.description.sponsorship | This research was partially funded by MCIN/AEI/10.13039/501100011033, NextGenerationEU/PRTR, FLATCITY-POC, Spain [grant number PDC2021-121239-C31]; MCIN/AEI/10.13039/501100011033 MAGIST, Spain [grant number PID2019-105221RB-C41]; Xunta de Galicia/FEDER-UE, Spain [grant numbers ED431G 2019/01, ED481A 2020/003, ED431C 2022/46, ED431C 2018/49 and ED431C 2021/53]. Funding for open access charge: Universidade da Coruña/CISUG . | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481A 2020/003 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2022/46 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2018/49 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2021/53 | es_ES |
dc.description.sponsorship | Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PDC2021-121239-C31/ES/FLATCITY-BOARD: BACKEND AND DASHBOARD FOR FLATCITY | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-105221RB-C41/ES/VISUALIZACION Y EXPLORACION BASADA EN FLUJOS Y ANALITICA DE BIG DATA ESPACIAL/ | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.iot.2023.100925 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | HAR | es_ES |
dc.subject | CNN | es_ES |
dc.subject | LSTM | es_ES |
dc.subject | Real life | es_ES |
dc.subject | Smartphones | es_ES |
dc.subject | Sensors | es_ES |
dc.title | Deep learning models for real-life human activity recognition from smartphone sensor data | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Internet of Things | es_ES |
UDC.volume | 24 | es_ES |
UDC.issue | 100925 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 22 | es_ES |
dc.identifier.doi | 10.1016/j.iot.2023.100925 |
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