{"ID":22952803,"CreatedAt":"2026-09-17T02:12:05.498442134Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.19127","arxiv_id":"2609.19127","title":"EarStreAM: A Closed-Loop Earable System for Personalized Stress-Adaptive Meditation","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.","short_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...","url_abs":"https://arxiv.org/abs/2609.19127","url_pdf":"https://arxiv.org/pdf/2609.19127v1","authors":"[\"Jonas Hummel\",\"Luisa Faust\",\"Elias Müller\",\"Eva Bertog\",\"Valeria Zitz\",\"Marius Johannes Prill\",\"Luca L. Bennardo\",\"Luisa Weber\",\"Tobias Röddiger\",\"Michael Beigl\"]","published":"2026-09-16T17:46:50Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[\"Large Language Model\"]","has_code":false}