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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| Published in: | Heliyon Vol. 10; no. 11; p. e31839 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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England
Elsevier Ltd
15.06.2024
Elsevier |
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| ISSN: | 2405-8440, 2405-8440 |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. 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. |
| ArticleNumber | e31839 |
| Author | Liu, Shuibin Wu, Heng Wen, Yangxing Fang, Zewen Li, Xiaoping He, Chunhua Lin, Juze |
| Author_xml | – sequence: 1 givenname: Chunhua orcidid: 0000-0003-0826-3596 surname: He fullname: He, Chunhua organization: School of Computer, Guangdong University of Technology, Guangzhou, 510000, PR China – sequence: 2 givenname: Zewen surname: Fang fullname: Fang, Zewen organization: School of Computer, Guangdong University of Technology, Guangzhou, 510000, PR China – sequence: 3 givenname: Shuibin surname: Liu fullname: Liu, Shuibin organization: School of Computer, Guangdong University of Technology, Guangzhou, 510000, PR China – sequence: 4 givenname: Heng orcidid: 0000-0003-0832-2218 surname: Wu fullname: Wu, Heng email: heng.wu@foxmail.com organization: School of Automation, Guangdong University of Technology, Guangzhou, 510000, PR China – sequence: 5 givenname: Xiaoping surname: Li fullname: Li, Xiaoping email: xpli@gdut.edu.cn organization: School of Computer, Guangdong University of Technology, Guangzhou, 510000, PR China – sequence: 6 givenname: Yangxing surname: Wen fullname: Wen, Yangxing email: wenyx5@mail.sysu.edu.cn organization: The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, PR China – sequence: 7 givenname: Juze surname: Lin fullname: Lin, Juze organization: Guangdong Provincial People's Hospital, Guangzhou, 510080, Guangdong, PR China |
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| Cites_doi | 10.3390/s19163472 10.1109/JSEN.2010.2089510 10.1109/JSEN.2020.3034207 10.1016/j.sleh.2023.07.016 10.1007/s12652-018-0796-1 10.1164/ajrccm.159.4.9806081 10.1038/s41598-017-07248-y 10.1145/3051124 10.1109/JSEN.2020.3007153 10.3390/s20143885 10.1016/j.pmcj.2013.10.008 10.1088/1361-6579/ab924b 10.7717/peerj.4849 10.3390/app11198896 10.1109/TNSRE.2023.3312396 10.3390/s22166220 10.1145/3397311 10.1109/JBHI.2018.2825020 10.3390/s23052475 10.1109/ACCESS.2017.2737461 10.1109/JSEN.2023.3262747 10.1378/chest.08-0812 10.1109/JSEN.2021.3059681 10.1109/TIM.2017.2779358 10.1111/j.1365-2702.2007.02109.x 10.1145/3264908 10.3390/iot2010007 10.3390/s20216307 10.3390/s19071731 10.1109/TNSRE.2020.3048121 10.1016/j.smrv.2010.02.001 10.1016/j.engappai.2023.106009 10.3390/electronics8070812 10.1109/JIOT.2022.3146926 10.1109/JSEN.2020.3043416 10.1186/s40001-018-0326-9 |
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| Keywords | Feature extraction Sleep posture detection Sleep monitoring belt Vital signals Machine learning |
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| SubjectTerms | accelerometers decision support systems Feature extraction Machine learning posture sleep Sleep monitoring belt Sleep posture detection statistical analysis support vector machines Vital signals wavelet |
| Title | A smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals |
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