A smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals

People spend approximately one-third of their lives in sleep, but more and more people are suffering from sleep disorders. Sleep posture is closely related to sleep quality, so related detection is very significant. In our previous work, a smart flexible sleep monitoring belt with MEMS triaxial acce...

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Vydáno v:Heliyon Ročník 10; číslo 11; s. e31839
Hlavní autoři: He, Chunhua, Fang, Zewen, Liu, Shuibin, Wu, Heng, Li, Xiaoping, Wen, Yangxing, Lin, Juze
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Elsevier Ltd 15.06.2024
Elsevier
Témata:
accelerometers
decision support systems
Feature extraction
Machine learning
posture
sleep
Sleep monitoring belt
Sleep posture detection
statistical analysis
support vector machines
Vital signals
wavelet
Feature extraction
Sleep posture detection
Sleep monitoring belt
Vital signals
Machine learning
ISSN:2405-8440, 2405-8440
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Shrnutí:People spend approximately one-third of their lives in sleep, but more and more people are suffering from sleep disorders. Sleep posture is closely related to sleep quality, so related detection is very significant. In our previous work, a smart flexible sleep monitoring belt with MEMS triaxial accelerometer and pressure sensor has been developed to detect the vital signs, snore events and sleep stages. However, the method for sleep posture detection has not been studied. Therefore, to achieve high performance, low cost and comfortable experience, this paper proposes a smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals measured by a MEMS Inertial Measurement Unit (IMU). Statistical analysis and wavelet packet transform are applied for the feature extraction of the vital sign signals. Then the algorithm of recursive feature elimination with cross-validation is introduced to further extract the key features. Besides, machine learning models with 10-fold cross validation process, such as decision tree, random forest, support vector machine, extreme gradient boosting and adaptive boosting, were adopted to recognize the sleep posture. 15 subjects were recruited to participate the experiment. Experimental results demonstrate that the detection accuracy of the random forest algorithm is the highest among the five machine learning models, which reaches 96.02 %. Therefore, the proposed sleep posture detection method based on the flexible sleep monitoring belt is feasible and effective.
Bibliografie:ObjectType-Article-1
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content type line 23
ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2024.e31839