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dc.contributor.authorKrejcar, Ondřej
dc.contributor.authorJirka, Jakub
dc.contributor.authorJanckulík, Dalibor
dc.date.accessioned2011-07-28T12:04:26Z
dc.date.available2011-07-28T12:04:26Z
dc.date.issued2011
dc.identifier.citationSensors. 2011, vol. 11, issue 6, p. 6037-6055.cs
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10084/89014
dc.description.abstractSleep is not just a passive process, but rather a highly dynamic process that is terminated by waking up. Throughout the night a specific number of sleep stages that are repeatedly changing in various periods of time take place. These specific time intervals and specific sleep stages are very important for the wake up event. It is far more difficult to wake up during the deep NREM (2–4) stage of sleep because the rest of the body is still sleeping. On the other hand if we wake up during the mild (REM, NREM1) sleep stage it is a much more pleasant experience for us and for our bodies. This problem led the authors to undertake this study and develop a Windows Mobile-based device application called wakeNsmile. The wakeNsmile application records and monitors the sleep stages for specific amounts of time before a desired alarm time set by users. It uses a built-in microphone and determines the optimal time to wake the user up. Hence, if the user sets an alarm in wakeNsmile to 7:00 and wakeNsmile detects that a more appropriate time to wake up (REM stage) is at 6:50, the alarm will start at 6:50. The current availability and low price of mobile devices is yet another reason to use and develop such an application that will hopefully help someone to wakeNsmile in the morning. So far, the wakeNsmile application has been tested on four individuals introduced in the final section.cs
dc.format.extent713194 bytescs
dc.format.mimetypeapplication/pdfcs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesSensorscs
dc.relation.urihttp://dx.doi.org/10.3390/s110606037cs
dc.subjectsleep stages detectioncs
dc.subjecthypnogramcs
dc.subjectWindows Mobilecs
dc.subjectFFT analysiscs
dc.titleUse of mobile phones as intelligent sensors for sound input analysis and sleep state detectioncs
dc.typearticlecs
dc.identifier.locationNení ve fondu ÚKcs
dc.identifier.doi10.3390/s110606037
dc.rights.accessopenAccess
dc.type.versionpublishedVersion
dc.identifier.wos000292026400030


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