2019 International Conference on Advances in the Emerging Computing Technologies (AECT)
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Abstract

Depression is a common, recurring mental disorder that causes significant impairment in people’s lives. In recent years, ubiquitous computing using mobile phones can monitor behavioral patterns relevant to depressive symptoms in-the-wild. In this paper, we propose data processing pipeline of short-term depression detection using mobile sensor data. We build a group model classified by depression severity for capturing depressive mood in a short-period time to handle data quality and data imbalance problem in a large-scale dataset. We expect the group model to identify and characterize digital phenotype representing each depressive group as a middle step toward personalization.
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