STDD: Short-Term Depression Detection with Passive Sensing

• Main Authors: Nematjon Narziev, Hwarang Goh, Kobiljon Toshnazarov, Seung Ah Lee, Kyong-Mee Chung, Youngtae Noh
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: Sensors
• Subjects: depression tracking; short-term detection; passive sensing; ema
• Online Access: https://www.mdpi.com/1424-8220/20/5/1396

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, <i>physical activity</i>, <i>mood</i>, <i>social activity</i>, <i>sleep</i>, and <i>food intake</i> and extracted features related to each symptom cluster from mobile devices&#8217; 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 <i>normal</i>, <i>mildly depressed</i>, <i>moderately depressed</i>, and <i>severely depressed</i> and built a machine learning model for automatic classification of depression category in a short period of time. To achieve the aim of short-term depression classification, we developed Short-Term Depression Detector (STDD), a framework that consisted of a smartphone and a wearable device that constantly reported the metrics (sensor data and self-reports) to perform depression group classification. The result of this pilot study revealed high correlations between participants` Ecological Momentary Assessment (EMA) self-reports and passive sensing (sensor data) in physical activity, mood, and sleep levels; STDD demonstrated the feasibility of group classification with an accuracy of 96.00% (standard deviation (SD) = 2.76).