EMMA: An Emotion-Aware Wellbeing Chatbot
This work addresses the need for automated, emotionally-aware mHealth interventions, though it is incremental as it builds on existing chatbot and sensor-based mood detection methods.
The paper tackled the problem of designing emotionally-aware chatbots for mental health interventions by developing EMMA, which provides personalized micro-activities based on mood detection from smartphone sensors, and found that users perceived it as likable in a two-week study with 39 participants.
The delivery of mental health interventions via ubiquitous devices has shown much promise. A conversational chatbot is a promising oracle for delivering appropriate just-in-time interventions. However, designing emotionally-aware agents, specially in this context, is under-explored. Furthermore, the feasibility of automating the delivery of just-in-time mHealth interventions via such an agent has not been fully studied. In this paper, we present the design and evaluation of EMMA (EMotion-Aware mHealth Agent) through a two-week long human-subject experiment with N=39 participants. EMMA provides emotionally appropriate micro-activities in an empathetic manner. We show that the system can be extended to detect a user's mood purely from smartphone sensor data. Our results show that our personalized machine learning model was perceived as likable via self-reports of emotion from users. Finally, we provide a set of guidelines for the design of emotion-aware bots for mHealth.