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Sensors 2011, 11(6), 6037-6055;

Use of Mobile Phones as Intelligent Sensors for Sound Input Analysis and Sleep State Detection

Department of Information Technologies, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
Department of Measurement and Control, Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, 17. Listopadu 15, Ostrava Poruba 70833, Czech Republic
Author to whom correspondence should be addressed.
Received: 4 May 2011 / Revised: 31 May 2011 / Accepted: 1 June 2011 / Published: 3 June 2011
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [696 KB, uploaded 21 June 2014]


Sleep 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. View Full-Text
Keywords: sleep stages detection; hypnogram; Windows Mobile; FFT analysis sleep stages detection; hypnogram; Windows Mobile; FFT analysis
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Krejcar, O.; Jirka, J.; Janckulik, D. Use of Mobile Phones as Intelligent Sensors for Sound Input Analysis and Sleep State Detection. Sensors 2011, 11, 6037-6055.

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