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

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 import...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Dalibor Janckulik [verfasserIn] Ondrej Krejcar [verfasserIn] Jakub Jirka [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	sleep stages detection hypnogram Windows Mobile FFT analysis

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 11(2011), 6, Seite 6037-6055
Übergeordnetes Werk:	volume:11 ; year:2011 ; number:6 ; pages:6037-6055

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s110606037

Katalog-ID:	DOAJ031621295

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520			\|a 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.
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source	In Sensors 11(2011), 6, Seite 6037-6055 volume:11 year:2011 number:6 pages:6037-6055
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callnumber-first	T - Technology
author	Dalibor Janckulik
spellingShingle	Dalibor Janckulik misc TP1-1185 misc sleep stages detection misc hypnogram misc Windows Mobile misc FFT analysis misc Chemical technology Use of Mobile Phones as Intelligent Sensors for Sound Input Analysis and Sleep State Detection
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topic_title	TP1-1185 Use of Mobile Phones as Intelligent Sensors for Sound Input Analysis and Sleep State Detection sleep stages detection hypnogram Windows Mobile FFT analysis
topic	misc TP1-1185 misc sleep stages detection misc hypnogram misc Windows Mobile misc FFT analysis misc Chemical technology
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title	Use of Mobile Phones as Intelligent Sensors for Sound Input Analysis and Sleep State Detection
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title_sort	use of mobile phones as intelligent sensors for sound input analysis and sleep state detection
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title_auth	Use of Mobile Phones as Intelligent Sensors for Sound Input Analysis and Sleep State Detection
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Use of Mobile Phones as Intelligent Sensors for Sound Input Analysis and Sleep State Detection
url	https://doi.org/10.3390/s110606037 https://doaj.org/article/891f6df3e63d438397a8d5a4706da8e6 http://www.mdpi.com/1424-8220/11/6/6037/ https://doaj.org/toc/1424-8220
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Use of Mobile Phones as Intelligent Sensors for Sound Input Analysis and Sleep State Detection

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