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.
@article{arxiv.1812.11423,
title = {EMMA: An Emotion-Aware Wellbeing Chatbot},
author = {Asma Ghandeharioun and Daniel McDuff and Mary Czerwinski and Kael Rowan},
journal= {arXiv preprint arXiv:1812.11423},
year = {2019}
}
Comments
Accepted for presentation at 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)