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.
Highlights
People spend almost one-third of their lifetime sleeping [1]
We used the Spine Base joint as the reference point in every frame to adjust the 3D coordinates of the different participants into the same coordinate system, which eliminated the differences in the relative positions of the participants and the Kinect
Since every subscale measured a different aspect of sleep quality [11], we explored the performance of the gait model to predict component scores
Summary
People spend almost one-third of their lifetime sleeping [1]. Adequate sleep is an important prerequisite for good health, while bad sleep can result in bad moods, inattention, fatigue, cardiovascular disease and even mortality [2,3,4,5]. People pay considerable attention to their sleep quality. To improve one’s sleep quality, people first need to know the exact condition of his/her sleep; that is to say, they need methods to monitor their sleep conditions. New technologies provide convenient methods for people to self-monitor and improve their sleep in their daily life [6, 7]. Sleep quality can be assessed by objective physical indicators and behavior or subjective perception.
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