# EMMA: An Emotion-Aware Wellbeing Chatbot

## TL;DR

This paper introduces EMMA, an emotion-aware chatbot for mental health support, demonstrating its ability to deliver empathetic interventions and accurately detect user mood from smartphone data in a two-week study.

## Contribution

It presents the design, implementation, and evaluation of EMMA, a novel emotionally-aware mHealth chatbot capable of delivering personalized micro-activities and mood detection.

## Key findings

- EMMA was perceived as likable by users based on self-reported emotions.
- The system successfully detects user mood from smartphone sensor data.
- Guidelines for designing emotion-aware mHealth chatbots are provided.

## Abstract

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.