Mirai: A Wearable Proactive AI "Inner-Voice" for Contextual Nudging
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3706599.3719881.pdf
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8.31 MB
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Author(s)
Fang, Cathy Mengying
Samaradivakara, Yasith
Maes, Pattie
Nanayakkara, Suranga
Date Issued
April 25, 2025
Publisher
ACM|Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
Citation
Cathy Mengying Fang, Yasith Samaradivakara, Pattie Maes, and Suranga Nanayakkara. 2025. Mirai: A Wearable Proactive AI "Inner-Voice" for Contextual Nudging. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '25). Association for Computing Machinery, New York, NY, USA, Article 399, 1–9.
Version
Final published version
Abstract
People often find it difficult to turn their intentions into real actions—a challenge that affects both personal growth and mental well-being. While established methods like cognitive-behavioral therapy and mindfulness training help people become more aware of their behaviors and set clear goals, these approaches cannot provide immediate guidance when people fall into automatic reactions or habits. We introduce Mirai, a novel wearable AI system with an integrated camera, real-time speech processing, and personalized voice-cloning to provide proactive and contextual nudges for positive behavior change. Mirai continuously monitors and analyzes the user’s environment to anticipate their intentions, generating contextually-appropriate responses delivered in the user’s own cloned voice. We demonstrate the application of Mirai through three scenarios focusing on dietary choices, work productivity, and communication skills. We also discuss future work on improving the proactive agent via human feedback and the need for a longitudinal study in naturalistic settings.
Description
CHI EA ’25, Yokohama, Japan
MIT Department
Massachusetts Institute of Technology. Media Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3706599.3719881
