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Mood disorders are common and associated with significant morbidity andmortality. Early diagnosis has the potential to greatly alleviate the burden ofmental illness and the ever increasing costs to families and society. Mobiledevices provide us a promising opportunity to detect the users' mood in anunobtrusive manner. In this study, we use a custom keyboard which collectskeystrokes' meta-data and accelerometer values. Based on the collected timeseries data in multiple modalities, we propose a deep personalized moodprediction approach, called {\pro}, by integrating convolutional and recurrentdeep architectures as well as exploring each individual's circadian rhythm.Experimental results not only demonstrate the feasibility and effectiveness ofusing smart-phone meta-data to predict the presence and severity of mooddisturbances in bipolar subjects, but also show the potential of personalizedmedical treatment for mood disorders.

dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction