000074947 001__ 74947 000074947 005__ 20200117221651.0 000074947 0247_ $$2doi$$a10.3390/s18082652 000074947 0248_ $$2sideral$$a107718 000074947 037__ $$aART-2018-107718 000074947 041__ $$aeng 000074947 100__ $$aGonzález-Landero, F. 000074947 245__ $$aGreen communication for tracking heart rate with smartbands 000074947 260__ $$c2018 000074947 5060_ $$aAccess copy available to the general public$$fUnrestricted 000074947 5203_ $$aThe trend of using wearables for healthcare is steeply increasing nowadays, and, consequently, in the market, there are several gadgets that measure several body features. In addition, the mixed use between smartphones and wearables has motivated research like the current one. The main goal of this work is to reduce the amount of times that a certain smartband (SB) measures the heart rate (HR) in order to save energy in communications without significantly reducing the utility of the application. This work has used an SB Sony 2 for measuring heart rate, Fit API for storing data and Android for managing data. The current approach has been assessed with data from HR sensors collected for more than three months. Once all HR measures were collected, then the current approach detected hourly ranges whose heart rate were higher than normal. The hourly ranges allowed for estimating the time periods of weeks that the user could be at potential risk for measuring frequently in these (60 times per hour) ranges. Out of these ranges, the measurement frequency was lower (six times per hour). If SB measures an unusual heart rate, the app warns the user so they are aware of the risk and can act accordingly. We analyzed two cases and we conclude that energy consumption was reduced in 83.57% in communications when using training of several weeks. In addition, a prediction per day was made using data of 20 users. On average, tests obtained 63.04% of accuracy in this experimentation using the training over the data of one day for each user. 000074947 536__ $$9info:eu-repo/grantAgreement/ES/DGA/IT1-8$$9info:eu-repo/grantAgreement/ES/UZ/JIUZ-2017-TEC-03 000074947 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000074947 590__ $$a3.031$$b2018 000074947 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b15 / 61 = 0.246$$c2018$$dQ1$$eT1 000074947 591__ $$aCHEMISTRY, ANALYTICAL$$b23 / 84 = 0.274$$c2018$$dQ2$$eT1 000074947 591__ $$aELECTROCHEMISTRY$$b12 / 26 = 0.462$$c2018$$dQ2$$eT2 000074947 592__ $$a0.592$$b2018 000074947 593__ $$aAnalytical Chemistry$$c2018$$dQ2 000074947 593__ $$aAtomic and Molecular Physics, and Optics$$c2018$$dQ2 000074947 593__ $$aMedicine (miscellaneous)$$c2018$$dQ2 000074947 593__ $$aElectrical and Electronic Engineering$$c2018$$dQ2 000074947 593__ $$aInstrumentation$$c2018$$dQ2 000074947 593__ $$aBiochemistry$$c2018$$dQ2 000074947 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000074947 700__ $$0(orcid)0000-0002-2726-6760$$aGarcía-Magariño, I.$$uUniversidad de Zaragoza 000074947 700__ $$0(orcid)0000-0002-4773-4904$$aLacuesta, R.$$uUniversidad de Zaragoza 000074947 700__ $$aLloret, J. 000074947 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000074947 773__ $$g18, 8 (2018), 2652 [21 pp]$$pSensors$$tSensors (Switzerland)$$x1424-8220 000074947 8564_ $$s480751$$uhttps://zaguan.unizar.es/record/74947/files/texto_completo.pdf$$yVersión publicada 000074947 8564_ $$s103427$$uhttps://zaguan.unizar.es/record/74947/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000074947 909CO $$ooai:zaguan.unizar.es:74947$$particulos$$pdriver 000074947 951__ $$a2020-01-17-22:09:01 000074947 980__ $$aARTICLE