dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction
This work addresses early diagnosis of mood disorders for patients and healthcare, but it is incremental as it builds on existing mobile sensing methods.
The paper tackled mood prediction in bipolar subjects by using smartphone keystroke and accelerometer data, achieving feasibility and effectiveness in detecting mood disturbances.
Mood disorders are common and associated with significant morbidity and mortality. Early diagnosis has the potential to greatly alleviate the burden of mental illness and the ever increasing costs to families and society. Mobile devices provide us a promising opportunity to detect the users' mood in an unobtrusive manner. In this study, we use a custom keyboard which collects keystrokes' meta-data and accelerometer values. Based on the collected time series data in multiple modalities, we propose a deep personalized mood prediction approach, called {\pro}, by integrating convolutional and recurrent deep architectures as well as exploring each individual's circadian rhythm. Experimental results not only demonstrate the feasibility and effectiveness of using smart-phone meta-data to predict the presence and severity of mood disturbances in bipolar subjects, but also show the potential of personalized medical treatment for mood disorders.