Gait can reveal sleep quality with machine learning models

Xingyun Liu, Bingli Sun, Zhan Zhang, Yameng Wang, Haina Tang and Tingshao Zhu

PLOS ONE, 2019, vol. 14, issue 9, 1-10

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

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0223012

DOI: 10.1371/journal.pone.0223012

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