Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
PLoS One. 2019 Sep 25;14(9):e0223012. doi: 10.1371/journal.pone.0223012. eCollection 2019.
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.
睡眠质量是一个重要的健康指标，目前的睡眠测量方法依赖于问卷、多导睡眠图等，这些方法具有侵入性、昂贵或耗时。因此，需要开发一种更非侵入性、更经济、更方便的方法。使用 Kinect 传感器来捕捉一个人的步态模式，可以揭示他/她的睡眠质量是否符合要求。
招募了 59 名没有残疾的健康学生作为参与者。使用匹兹堡睡眠质量指数（PSQI）和 Kinect 传感器来获取睡眠质量评分和步态数据。在数据预处理后，提取步态特征，用于训练机器学习模型，根据数据预测睡眠质量评分。t 检验表明，以下关节在预测中具有更强的权重：头部、脊柱、肩部、左手腕、右手、左手拇指、右手拇指、左手指尖、左髋和左脚。
对于睡眠质量预测，高斯过程的效果最好，相关性为 0.78（p < 0.001）。对于子量表，线性回归的最佳结果是日间功能障碍为 0.51（p < 0.001）。步态可以很好地反映睡眠质量。这种方法是识别睡眠质量的现有方法的一个很好的补充，它更具生态性，侵入性更小。
