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Gait can reveal sleep quality with machine learning models
Liu, Xingyun1,2,3; Sun, Bingli1; Zhang, Zhan1,4; Wang, Yameng1,4; Tang, Haina5; Zhu, Tingshao1
First AuthorLiu, Xingyun
2019-09-25
Source PublicationPLOS ONE
Correspondent Emailtszhu@psych.ac.cn(zhu, tingshao)
ISSN1932-6203
Subtypearticle
Volume14Issue:9Pages:10
Contribution Rank1
Abstract

Sleep quality is an important health indicator, and the current measurements of sleep rely on questionnaires, polysomnography, etc., which are intrusive, expensive or time consuming. Therefore, a more nonintrusive, inexpensive and convenient method needs to be developed. Use of the Kinect sensor to capture one's gait pattern can reveal whether his/her sleep quality meets the requirements. Fifty-nine healthy students without disabilities were recruited as participants. The Pittsburgh Sleep Quality Index (PSQI) and Kinect sensors were used to acquire the sleep quality scores and gait data. After data preprocessing, gait features were extracted for training machine learning models that predicted sleep quality scores based on the data. The t-test indicated that the following joints had stronger weightings in the prediction: the Head, Spine Shoulder, Wrist Left, Hand Right, Thumb Left, Thumb Right, Hand Tip Left, Hip Left, and Foot Left. For sleep quality prediction, the best result was achieved by Gaussian processes, with a correlation of 0.78 (p < 0.001). For the subscales, the best result was 0.51 for daytime dysfunction (p < 0.001) by linear regression. Gait can reveal sleep quality quite well. This method is a good supplement to the existing methods in identifying sleep quality more ecologically and less intrusively.

DOI10.1371/journal.pone.0223012
Indexed BySCI
Language英语
Funding OrganizationNational Basic Research Program of China ; China Social Science Fund ; National Social Science Fund of China ; Key Research Program of the Chinese Academy of Sciences ; Chinese Academy of Sciences project
Funding ProjectNational Basic Research Program of China[2014CB744600] ; China Social Science Fund[Y8JJ183010] ; National Social Science Fund of China[16AZD058] ; Key Research Program of the Chinese Academy of Sciences[ZDRW-XH-2019-4] ; Chinese Academy of Sciences project[CXJJ-16M119]
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000489147400001
PublisherPUBLIC LIBRARY SCIENCE
WOS KeywordENERGY-EXPENDITURE ; POOR SLEEP ; RECOGNITION ; PERFORMANCE ; DURATION ; HEALTH ; INDEX
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/30183
Collection社会与工程心理学研究室
Corresponding AuthorZhu, Tingshao
Affiliation1.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
3.City Univ Hong Kong, Dept Social & Behav Sci, Hong Kong, Peoples R China
4.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Liu, Xingyun,Sun, Bingli,Zhang, Zhan,et al. Gait can reveal sleep quality with machine learning models[J]. PLOS ONE,2019,14(9):10.
APA Liu, Xingyun,Sun, Bingli,Zhang, Zhan,Wang, Yameng,Tang, Haina,&Zhu, Tingshao.(2019).Gait can reveal sleep quality with machine learning models.PLOS ONE,14(9),10.
MLA Liu, Xingyun,et al."Gait can reveal sleep quality with machine learning models".PLOS ONE 14.9(2019):10.
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