Cluster-based Approach to Improve Affect Recognition from Passively Sensed Data

The extent to which individuals experience positive and negative affect on a daily basis is associated with mental health outcomes [1]. Higher levels of negative affect are associated with increased vulnerability to many mental disorders, including depression and anxiety disorders, two of the most common types of mental disorders in U.S. adults [2]. Mental health research typically relies on self-report questionnaires that assess negative affect at a moment in time. Repeated administration of these measures, such as in an ecological momentary assessment (EMA) framework, is resource intensive and susceptible to retrospective bias when participants are asked to recall their mood over a previous duration [3]. Ideally, negative affect would be recognized without asking participants, thereby reducing burden, improving compliance among participants, and allowing for continuous modeling of affect change.