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. 2018 Nov 6;17(Suppl 2):157.
doi: 10.1186/s12938-018-0584-3.

Sleeping posture recognition using fuzzy c-means algorithm

Rong-Shue Hsiao  1 , Tian-Xiang Chen  2 , Mekuanint Agegnehu Bitew  2 , Chun-Hao Kao  2 , Tzu-Yu Li  2
Affiliations

Sleeping posture recognition using fuzzy c-means algorithm

Rong-Shue Hsiao et al. Biomed Eng Online. .

Abstract

Background: Pressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. However, lower limbs cannot be recognized accurately unless thousands of sensors are deployed on the bedsheet.

Method: We designed a sleeping posture recognition scheme in which FSR sensors were deployed on the upper part of the bedsheet to record the pressure distribution of the upper body. In addition, an infrared array sensor was deployed to collect data for the lower body. Posture recognition was performed using a fuzzy c-means clustering algorithm. Six types of sleeping body posture were recognized from the combination of the upper and lower body postures.

Results: The experimental results showed that the proposed method achieved an accuracy of above 88%. Moreover, the proposed scheme is cost-efficient and easy to deploy.

Conclusions: The proposed sleeping posture recognition system can be used for pressure ulcer prevention and sleep quality assessment. Compared to wearable sensors and cameras, FSR sensors and infrared array sensors are unobstructed and meet privacy requirements. Moreover, the proposed method provides a cost-effective solution for the recognition of sleeping posture.

Keywords: Force-sensing resistor; Fuzzy logic; Infrared array sensor; Sleeping posture recognition.

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Figures

Fig. 1
Fig. 1
Sensor systems employed in this study: a FSR sensors; b infrared array sensor
Fig. 2
Fig. 2
Grid-EYE detection system
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Fig. 3
Different levels of data-processing for sleeping posture recognition
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Fig. 4
Sleeping posture classification: a right log; b right fetus; c supine; d prone; e left fetus; f left log
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Fig. 5
Histogram projections of vertical and horizontal axes
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Fig. 6
Middle point determination of the trunk using FCM
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Body-part mapping
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Pressure distributions of the supine and left lateral decubitus positions
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Temperature distributions of the left log and left fetus positions
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Sleeping posture classification using a decision tree
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Feature space of trunk symmetry
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Feature space of leg symmetry
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Experimental setup
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Fig. 14
Sensing gap in pressure sensor arrays
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Fig. 15
Deployment comparison: a pressure sensors; b infrared array sensor
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Fig. 16
Sensing grid of pressure sensor array deployment for signal detection threshold calculation
See this image and copyright information in PMC

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