Gait can reveal sleep quality with machine learning models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

  • Bingli Sun
  • Zhan Zhang
  • Yameng Wang
  • Haina Tang
  • Tingshao Zhu

Detail(s)

Original languageEnglish
Article numbere0223012
Journal / PublicationPLoS ONE
Volume14
Issue number9
Online published25 Sep 2019
Publication statusPublished - 2019

Link(s)

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.

Research Area(s)

Citation Format(s)

Gait can reveal sleep quality with machine learning models. / Liu, Xingyun; Sun, Bingli; Zhang, Zhan; Wang, Yameng; Tang, Haina; Zhu, Tingshao.

In: PLoS ONE, Vol. 14, No. 9, e0223012, 2019.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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