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.

Background As smartphone has been widely used, understanding how depression correlates with social behavior on smartphones can be beneficial for early diagnosis of depression. An enormous amount of research relied on self-report questionnaires, which is not objective. Only recently the increased availability of rich data about human behavior in digital space has provided new perspectives for the investigation of individual differences. Objective The objective of this study was to explore depressed Chinese individuals’ social behavior in digital space through metadata collected via smartphones. Methods A total of 120 participants were recruited to carry a smartphone with a metadata collection app (MobileSens). At the end of metadata collection, they were instructed to complete the Center for Epidemiological Studies-Depression Scale (CES-D). We then separated participants into nondepressed and depressed groups based on their scores on CES-D. From the metadata of smartphone usage, we extracted 44 features, including traditional social behaviors such as making calls and sending SMS text messages, and the usage of social apps (eg, WeChat and Sina Weibo, 2 popular social apps in China). The 2-way ANOVA (nondepressed vs depressed × male vs female) and multiple logistic regression analysis were conducted to investigate differences in social behaviors on smartphones among users. Results The results found depressed users received less calls from contacts (all day: F1,116=3.995, P=.048, η2=0.033; afternoon: F1,116=5.278, P=.02, η2=0.044), and used social apps more frequently (all day: F1,116=6.801, P=.01, η2=0.055; evening: F1,116=6.902, P=.01, η2=0.056) than nondepressed ones. In the depressed group, females used Weibo more frequently than males (all day: F1,116=11.744, P=.001, η2=0.092; morning: F1,116=9.105, P=.003, η2=0.073; afternoon: F1,116=14.224, P<.001, η2=0.109; evening: F1,116=9.052, P=.003, η2=0.072). Moreover, usage of social apps in the evening emerged as a predictor of depressive symptoms for all participants (odds ratio [OR] 1.007, 95% CI 1.001-1.013; P=.02) and male (OR 1.013, 95% CI 1.003-1.022; P=.01), and usage of Weibo in the morning emerged as a predictor for female (OR 1.183, 95% CI 1.015-1.378; P=.03). Conclusions This paper finds that there exists a certain correlation between depression and social behavior on smartphones. The result may be useful to improve social interaction for depressed individuals in the daily lives and may be insightful for early diagnosis of depression.

As adapting vehicles to drivers’ preferences has become an important focus point in the automotive sector, a more convenient, objective, real-time method for identifying drivers’ personality traits is increasingly important. Only recently has increased availability of driving signals obtained via controller area network (CAN) bus provided new perspectives for investigating personality differences. This study proposes a new methodology for identifying drivers’ Big Five personality traits through driving signals, specifically accelerator pedal angle, frontal acceleration, steering wheel angle, lateral acceleration, and speed. Data were collected from 92 participants who were asked to drive a car along a pre-defined 15 km route. Using statistical methods and the discrete Fourier transform, some time-frequency features related to driving were extracted to establish models for identifying participants’ Big Five personality traits. For these five personality trait dimensions, the coefficients of determination of effective predictive models were between 0.19 and 0.74, the root mean squared errors were between 2.47 and 4.23, and the correlations between predicted scores and self-reported questionnaire scores were considered medium to strong (0.56–0.88). The results showed that personality traits can be revealed through driving signals, and time-frequency features extracted from driving signals are effective in characterizing and identifying Big Five personality traits. This approach could be of potential value in the development of in-car integration or driver assistance systems and indicates a possible direction for further research on convenient psychometric methods.