EarStreAM: A Closed-Loop Earable System for Personalized Stress-Adaptive Meditation
Jonas Hummel, Luisa Faust, Elias Müller, Eva Bertog, Valeria Zitz, Marius Johannes Prill, Luca L. Bennardo, Luisa Weber, Tobias Röddiger, Michael Beigl
arXiv Preprint Archive September 16, 2026 preprint
Study at a glance
AI-extracted from the abstract| Characteristics | Demo |
|---|---|
| Topics | Meditation |
| Keywords | Cs.hc |
| Key points | The authors present EarStreAM, a closed-loop earable that uses in-ear heart rate and heart rate variability to detect elevated stress and then delivers an LLM-generated guided meditation adapted in real time to the user's stress state. The demo offers a biosignal-adaptive mode with optional stress induction and a meditation-only mode highlighting generative personalization, aimed at real-time stress support in demanding office work. |
Abstract
We present EarStreAM, a closed-loop earable system for stress-adaptive meditation that integrates in-ear physiological sensing with personalized, real-time intervention. Leveraging OpenEarable 2.0's multimodal sensing, EarStreAM continuously monitors physiological signals and detects elevated stress from heart rate and heart rate variability. Upon detection, the system initiates a personalized guided meditation generated by an LLM and adapted in real time to the user's stress state. The demo offers a hands-on experience of stress-adaptive meditation in two modes: a biosignal-adaptive meditation with optional stress induction to illustrate closed-loop adaptation, and a meditation-only mode focusing on EarStreAM's generative personalization capabilities. The demo highlights how in-ear sensing, closed-loop adaptation, and personalized generative meditation can be integrated into an earable system for real-time stress support in demanding office work contexts.