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dc.contributor.authorSanz Moreno, José 
dc.contributor.authorGómez Pulido, José Manuel 
dc.contributor.authorGarcés Jiménez, Alberto 
dc.contributor.authorCalderón Gómez, Huriviades 
dc.contributor.authorVargas Lombardo, Miguel 
dc.contributor.authorCastillo Sequera, José Luis 
dc.contributor.authorPolo Luque, María Luz 
dc.contributor.authorToro Flores, Rafael 
dc.contributor.authorSención, Gloria
dc.date.accessioned2023-03-06T16:08:07Z
dc.date.available2023-03-06T16:08:07Z
dc.date.issued2020-01-30
dc.identifier.bibliographicCitationSanz Moreno, J., Gómez Pulido, J., Garcés, A., Calderón Gómez, H., Vargas Lombardo, M., Castillo Sequera, J.L., Polo Luque, M.L., Toro, R. & Sención Martínez, G. 2020, “mHealth system for the early detection of infectious diseases using biomedical signals”, in Proceedings of the Latin American Congress on Automation and Robotics, LACAR 2019, Advances in Automation and Robotics Research, pp. 203-213.
dc.identifier.isbn978-3-030-40309-6
dc.identifier.urihttp://hdl.handle.net/10017/56052
dc.descriptionLatin American Congress on Automation and Robotics LACAR 2019, 30/10/2019-01/11/2019, Cali, Colombia.
dc.description.abstractDetection at an early stage of an infection is a major clinical challenge. An infection that is not diagnosed in time can not only seriously affect the health of the infected patient, but also spread and initiate a contagious approach towards other people. This paper deals with mHealth system for medical care and pre-diagnosis. The developed mHealth system use an Android App that collects physiological signals from the patients with a portable and easy-to-use sensors kit. The focus of the work is put on being able to build a low-cost system that using a very small amounts of data (one set record per patient and day). The processed data are uploaded to an online database to train a clinical decision support system to automatically diagnose infections. The mHealth system may be operated by the same personnel on site not requiring to be medical or computational skilled at all. The implementation takes five kinds of measures simultaneously (Electrodermal Activity, Body Temperature, Blood Pressure, Heart Beat Rate and Oxygen Saturation (SPO2)). A real implementation has been tested and results confirm that the sampling process can be done very fast and steadily Finally, the App usability was tested, showing a fast learning curve and no significant differences are observable in learning time by people with different skills or age. These usability factors are key for the mHealth system success.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherSpringer
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)*
dc.rights© 2020 Springer Nature
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titlemHealth system for the early detection of infectious diseases using biomedical signalsen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.subject.ecienciaInformáticaen
dc.subject.ecienciaComputer scienceen
dc.subject.ecienciaMedicinaes_ES
dc.subject.ecienciaMedicineen
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Ciencias de la Computaciónes_ES
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Enfermería y Fisioterapiaes_ES
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Medicina y Especialidades Médicases_ES
dc.date.updated2023-03-06T16:06:44Z
dc.relation.publisherversionhttps://doi.org/10.1007/978-3-030-40309-6_20
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.1007/978-3-030-40309-6_20
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.uxxiCC/0000038099
dc.identifier.publicationtitleAdvances in Automation and Robotics Research. Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 112)
dc.identifier.publicationlastpage213
dc.identifier.publicationfirstpage203


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