STDD: Short-Term Depression Detection with Passive Sensing (original) (raw)

It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely, physical activity, mood, social activity, sleep, and food intake and extracted features related to each symptom cluster from mobile devices’ sensors. We conducted an experiment, where we recruited 20 participants from four different depression groups based on PHQ-9 (the Patient Health Questionnaire-9, the 9-item depression module from the full PHQ), which are normal, mildly depressed, moderately depressed, and severely depressed and built a machine ...