Gait can reveal sleep quality with machine learning models


Autoři: Xingyun Liu aff001;  Bingli Sun aff001;  Zhan Zhang aff001;  Yameng Wang aff001;  Haina Tang aff005;  Tingshao Zhu aff001
Působiště autorů: Institute of Psychology, Chinese Academy of Sciences, Beijing, China aff001;  Department of Psychology, University of Chinese Academy of Sciences, Beijing, China aff002;  Department of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, China aff003;  School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China aff004;  School of Artificial Intelligence, University of Chinese Academy of Science, Beijing, China aff005
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223012

Souhrn

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.

Klíčová slova:

Gait analysis – Hands – Linear regression analysis – Machine learning – Skeletal joints – Sleep – Walking – Thumbs


Zdroje

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PLOS One


2019 Číslo 9

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