EMMA: an emotion-aware wellbeing chatbot
Open accessAbstract
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.
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Apps and accompanying wearable devices can tailor activities or wellness suggestions to users in response to their changing emotional state in a way similar to customized plans by human healthcare professionals.
“This allows apps and accompanying wearable devices to tailor activities or wellness suggestions to users in response to their changing emotional state (63) in a way that is similar to providing customized plans by human healthcare professionals.”
From Digital Therapeutic Alliance With Fully Automated Mental Health Smartphone Apps: A Narrative Review (2022) · cites this paper as [63]
The Method and Wellbeing Interventions sections describe a mobile app that infers mood from phone sensors and selects wellness activities for different emotional states. The Introduction mentions wearable sensors as useful for detecting affect, but the paper does not show an accompanying wearable device tailoring suggestions. The comparison with human healthcare professionals appears after the citation and is the citing authors’ analogy, not a finding attributed to this paper. (LLM, conf=0.85)
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References 48 · Cited by 126
Research works printed but not linked: 3
- L. Ring, T. Bickmore, and P. Pedrelli, "An affectively aware virtual therapist for depression counseling," in Proceedings of the CHI 2016 workshop on Computing and Mental Health, 2016.
- --, "Can your smartphone infer your mood," in PhoneSense work- shop, 2011, pp. 1-5.
- A. Ghandeharioun and R. Picard, "Brightbeat: Effortlessly influencing breathing for cultivating calmness and focus," in Ext. Abstracts CHI. ACM, 2017, pp. 1624-1631.
- Authors
- Ghandeharioun, A., McDuff, D., Czerwinski, M., & Rowan, K.
- Affiliations
- MIT Media Lab (US)
- Microsoft (United States) (US)
- Venue
- 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)
- Publisher
- IEEE
- Published
- 2019-09
- Pages
- 1-7
- ISSN
- 2156-8103 (ISSN-L)
- OpenAlex
- W2962969859
- Cited by
- 126
- References
- 48
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 20 · 2023: 22 · 2022: 23 · 2021: 17 · 2020: 12
- FWCI
- 33.64
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Conference on Affective Computing and Intelligent Interaction and workshops (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- mental health interventions, mobile health, mood detection, smartphone sensor data, empathetic chatbots
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