The delivery of mental health interventions via ubiquitous devices has shown a lot of promise. A natural conversational interface that allows longitudinal symptom tracking and appropriate just-in-time interventions would be extremely valuable. However, the task of designing emotionally aware agents is still poorly understood. Furthermore, the feasibility of automating the delivery of just-in-time mHealth interventions via such an agent has not been fully studied. In this project, we explore the design and evaluation of EMMA (EMotion-Aware mHealth Agent).
EMMA conducts experience sampling in an empathetic manner and provides emotionally appropriate micro-activities. We show the system can be extended to detect a user's mood purely from smartphone sensor data.
We have conducted a three-week user study (N=58). Our results show that extroverts preferred EMMA significantly more, and that our personalized machine learning model was effective, as was relying on ground-truth emotion samples from users.