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A Smart System for Sleep Monitoring by Integrating IoT With Big Data Analytics

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A Smart System for Sleep Monitoring by Integrating IoT With Big Data Analytics

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dc.contributor.author Yacchirema-Vargas, Diana Cecilia es_ES
dc.contributor.author Sarabia-Jácome, David Fernando es_ES
dc.contributor.author Palau Salvador, Carlos Enrique es_ES
dc.contributor.author Esteve Domingo, Manuel es_ES
dc.date.accessioned 2019-07-06T20:02:24Z
dc.date.available 2019-07-06T20:02:24Z
dc.date.issued 2018 es_ES
dc.identifier.uri http://hdl.handle.net/10251/123260
dc.description.abstract [EN] Obtrusive sleep apnea (OSA) is one of the most important sleep disorders because it has a direct adverse impact on the quality of life. Intellectual deterioration, decreased psychomotor performance, behavior, and personality disorders are some of the consequences of OSA. Therefore, a real-time monitoring of this disorder is a critical need in healthcare solutions. There are several systems for OSA detection. Nevertheless, despite their promising results, these systems not guiding their treatment. For these reasons, this research presents an innovative system for both to detect and support of treatment of OSA of elderly people by monitoring multiple factors such as sleep environment, sleep status, physical activities, and physiological parameters as well as the use of open data available in smart cities. Our system architecture performs two types of processing. On the one hand, a pre-processing based on rules that enables the sending of real-time notifications to responsible for the care of elderly, in the event of an emergency situation. This pre-processing is essentially based on a fog computing approach implemented in a smart device operating at the edge of the network that additionally offers advanced interoperability services: technical, syntactic, and semantic. On the other hand, a batch data processing that enables a descriptive analysis that statistically details the behavior of the data and a predictive analysis for the development of services, such as predicting the least polluted place to perform outdoor activities. This processing uses big data tools on cloud computing. The performed experiments show a 93.3% of effectivity in the air quality index prediction to guide the OSA treatment. The system's performance has been evaluated in terms of latency. The achieved results clearly demonstrate that the pre-processing of data at the edge of the network improves the efficiency of the system. es_ES
dc.description.sponsorship This work was supported in part by the European Union's Horizon 2020 Research and Innovation Programme through the Interoperability of Heterogeneous IoT Platforms Project (INTER-IoT) under Grant 687283, in part by the Escuela Politecnica Nacional, Ecuador, and in part by the Secretaria Nacional de Educacion Superior, Ciencia, Tecnologia e Innovacion (SENESCYT), Ecuador. es_ES
dc.language Inglés es_ES
dc.publisher Institute of Electrical and Electronics Engineers es_ES
dc.relation.ispartof IEEE Access es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Internet-of-Things es_ES
dc.subject Big data es_ES
dc.subject Interoperability es_ES
dc.subject Sleep monitoring es_ES
dc.subject Health monitoring es_ES
dc.subject Open data es_ES
dc.subject Fog computing es_ES
dc.subject Cloud computing es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title A Smart System for Sleep Monitoring by Integrating IoT With Big Data Analytics es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1109/ACCESS.2018.2849822 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/687283/EU es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Yacchirema-Vargas, DC.; Sarabia-Jácome, DF.; Palau Salvador, CE.; Esteve Domingo, M. (2018). A Smart System for Sleep Monitoring by Integrating IoT With Big Data Analytics. IEEE Access. 6:35988-36001. https://doi.org/10.1109/ACCESS.2018.2849822 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1109/ACCESS.2018.2849822 es_ES
dc.description.upvformatpinicio 35988 es_ES
dc.description.upvformatpfin 36001 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 6 es_ES
dc.identifier.eissn 2169-3536 es_ES
dc.relation.pasarela S\369462 es_ES
dc.contributor.funder European Commission es_ES


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