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. 2022 Nov 29;22(23):9296.
doi: 10.3390/s22239296.

Sleep Pattern Analysis in Unconstrained and Unconscious State

Won-Ho Jun  1 , Hyung-Ju Kim  1 , Youn-Sik Hong  1
Affiliations

Sleep Pattern Analysis in Unconstrained and Unconscious State

Won-Ho Jun et al. Sensors (Basel). .

Abstract

Sleep accounts for one-third of an individual's life and is a measure of health. Both sleep time and quality are essential, and a person requires sound sleep to stay healthy. Generally, sleep patterns are influenced by genetic factors and differ among people. Therefore, analyzing whether individual sleep patterns guarantee sufficient sleep is necessary. Here, we aimed to acquire information regarding the sleep status of individuals in an unconstrained and unconscious state to consequently classify the sleep state. Accordingly, we collected data associated with the sleep status of individuals, such as frequency of tosses and turns, snoring, and body temperature, as well as environmental data, such as room temperature, humidity, illuminance, carbon dioxide concentration, and ambient noise. The sleep state was classified into two stages: nonrapid eye movement and rapid eye movement sleep, rather than the general four stages. Furthermore, to verify the validity of the sleep state classifications, we compared them with heart rate.

Keywords: NREM; REM; sleep pattern; sleep posture; unconscious; unrestraint.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Sleep stages.
Figure 2
Figure 2
Sleep-related information for sleep pattern analysis.
Figure 3
Figure 3
Overall architecture of the sleep pattern monitoring system using nonwearable sensors.
Figure 4
Figure 4
Smart pillow and the three sleeping postures it can distinguish. (a) Horizontal arrangement of eight FSR sensors; (b) sleeping postures that can be discriminated using the smart pillow.
Figure 5
Figure 5
Sampling of sleeping posture images using an infrared camera. (a) Infrared camera module; (b) sleep posture images.
Figure 6
Figure 6
Set of three sensor modules attached to the ceiling.
Figure 7
Figure 7
Hardware configuration for sound detection and recording.
Figure 8
Figure 8
CO2 concentration measurement module.
Figure 9
Figure 9
Band-type heart rate-measuring device.
Figure 10
Figure 10
Average pressure intensities of the FSR sensors with respect to the sleeping posture.
Figure 11
Figure 11
Various pressure distributions for each sleeping posture, each FSR sensor (above), and the fourth and the fifth sensors (below).
Figure 12
Figure 12
Sleep pattern measurement results (measurement period: 05:03:46 a.m. to 07:52:45 a.m. 22 October 2021).
Figure 13
Figure 13
Snapshots of the left-to-right tossing.
Figure 14
Figure 14
Changes in heart rate according to the frequency of tossing and turning.
Figure 15
Figure 15
Sound detection of snoring interval (15 s): 01:57:50 a.m. to 01:58:05 a.m. (left) and 02:00:50 a.m. to 02:01:05 a.m. (right).
Figure 16
Figure 16
Comparison of snoring sound and heart rate.
Figure 17
Figure 17
Comparison of snoring sound and carbon dioxide concentration.
Figure 18
Figure 18
User interface screen of the sleep pattern monitoring system.
Figure 19
Figure 19
Sleep-related information in the section predicted by the NREM sleep stage. (a) Sleep-related information during the predicted NREM sleep stage; (b) 22 snapshots of sleep postures captured from 04:34:38 to 04:34:59.
Figure 20
Figure 20
Sleep-related information in the section predicted by the REM sleep stage. (a) Sleep-related information during the predicted REM sleep stage; (b) 22 snapshots of sleep postures captured from 05:32:14 to 05:32:36.
Figure 21
Figure 21
Summary of sleep information obtained during sleep. (a) Summary of the participant’s sleep information; (b) Summary of sleep information for the two sleep stages.
Figure 22
Figure 22
ROC of the logistic regression and decision tree classifiers.
Figure 23
Figure 23
Comparison of the measurement results of the application and SPMS.
Figure 24
Figure 24
Sleep pattern measurement results of the 56-year-old woman.
Figure 25
Figure 25
Sleep pattern measurement results of the 27-year-old man.
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