Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone Abstract   The number of individuals with mental disorders is increasing and they are commonly found among individuals who avoid social interaction and like to live alone. Amongst such mental health disorders is depression which is both common and serious. The present paper introduces a method to assess the depression level of an individual using a smartphone by monitoring their daily activities. The time domain characteristics from a smartphone acceleration sensor were used alongside a vector machine algorithm to classify physical activities. Additionally, the geographical location information was clustered using a smartphone GPS sensor to simplify movement patterns. A total of 12 features were extracted from individualsxe2x80x99 physical activity and movement patterns and were analyzed alongside their weekly depression scores using the nineitem Patient Health Questionnaire. Using a wrapper feature selection method, a subset of features was selected and applied to a linear regression model to estimate the depression score. The support vector machine algorithm was then used to classify the depression severity level among individuals (absence, moderate, severe) and had an accuracy of 87.2% in severe depression cases which outperformed other classification models including the knearest neighbor and artificial neural network. This method of identifying depression is a costeffective solution for longterm use and can monitor individuals for depression without invading their personal space or creating other daytoday disturbances.

REALTime Smartphone Activity Classification Using Inertial Sensorsxe2x80x94Recognition of Scrolling, Typing, and Watching Videos While Sitting or Walking By developing awareness of smartphone activities that the user is performing on their smartphone, such as scrolling feeds, typing and watching videos, we can develop application features that are beneficial to the users, such as personalization. It is currently not possible to access realtime smartphone activities directly, due to standard smartphone privileges and if internal movement sensors can detect them, there may be implications for access policies. Our research seeks to understand whether the sensor data from existing smartphone inertial measurement unit (IMU) sensors (triaxial accelerometers, gyroscopes and magnetometers) can be used to classify typical human smartphone activities. We designed and conducted a study with human participants which uses an Android app to collect motion data during scrolling, typing and watching videos, while walking or seated and the baseline of smartphone nonuse, while sitting and walking. We then trained a machine learning (ML) model to perform realtime activity recognition of those eight states. We investigated various algorithms and parameters for the best accuracy. Our optimal solution achieved an accuracy of 78.6% with the Extremely Randomized Trees algorithm, data sampled at 50 Hz and 5s windows. We conclude by discussing the viability of using IMU sensors to recognize common smartphone activities.