000075427 001__ 75427
000075427 005__ 20181123110452.0
000075427 0247_ $$2doi$$a10.22489/CinC.2017.137-402
000075427 0248_ $$2sideral$$a107424
000075427 037__ $$aART-2017-107424
000075427 041__ $$aeng
000075427 100__ $$aLandreani, F.
000075427 245__ $$aRespiratory frequency estimation from accelerometric signals acquired by mobile phone in a controlled breathing protocol
000075427 260__ $$c2017
000075427 5060_ $$aAccess copy available to the general public$$fUnrestricted
000075427 5203_ $$aThe aim of this work was to test if the smartphone’s embedded triaxial accelerometer can be used to extract respiratory frequency information from the chest movements during a controlled breathing protocol. Respiratory signals from 10 young volunteers were recorded simultaneously, by two smartphones (iPhone 4s and 6s; sampling frequency ~100 Hz), positioned one on the sternum and one on the belly, while in supine posture. At the same time, a belt transducer was used to acquire the reference respiratory signal. A controlled breathing protocol, consisting of four consecutive phases of 12 respiratory cycles each (respiratory frequencies at 0.25, 0.17, 0.125 and 0.1 Hz), was imposed through the visualization of a moving bar on a display. After low-pass filtering (fc=0.5 Hz), the respiratory signal was obtained from both smartphones, and respiratory frequency derived for each phase. Compared to the belt transducer, the resulting error was lower than 2% for each imposed respiratory frequency, for both smartphones’ positions, with better results obtained for the smartphone positioned above the belly.
000075427 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000075427 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000075427 700__ $$0(orcid)0000-0003-0226-4950$$aMartin-Yebra, A.$$uUniversidad de Zaragoza
000075427 700__ $$aCasellato, C.
000075427 700__ $$aPavan, E.
000075427 700__ $$aFrigo, C.
000075427 700__ $$aMigeotte, P.-F.
000075427 700__ $$aFaini, A
000075427 700__ $$aParati, G.
000075427 700__ $$aCaiani, E.G.
000075427 7102_ $$15008$$2X$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cProy. investigación JBA
000075427 773__ $$g44 (2017), [4 pp.]$$pComput. cardiol.$$tComputing in Cardiology$$x2325-8861
000075427 8564_ $$s584181$$uhttps://zaguan.unizar.es/record/75427/files/texto_completo.pdf$$yVersión publicada
000075427 8564_ $$s112411$$uhttps://zaguan.unizar.es/record/75427/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000075427 909CO $$ooai:zaguan.unizar.es:75427$$particulos$$pdriver
000075427 951__ $$a2018-11-23-11:02:27
000075427 980__ $$aARTICLE