Public articles linked to the same research event.
arXiv This work presents EarStreAM, a closed-loop earable system built on OpenEarable 2.0 and a companion smartphone app that continuously monitors in-ear PPG to derive heart rate and heart rate variability as stress proxies, triggers an LLM-generated personalized guided meditation that adapts in real time to the user's physiological state, and terminates it once stress returns to baseline, demonstrated in two modes: a biosignal-adaptive mode with optional stress induction and a meditation-only mode.
This work presents EarStreAM, a closed-loop earable system built on OpenEarable 2.0 and a companion smartphone app that continuously monitors in-ear PPG to derive heart rate and heart rate variability as stress proxies, triggers an LLM-generated personalized guided meditation that adapts in real time to the user's physiological state, and terminates it once stress returns to baseline, demonstrated in two modes: a biosignal-adaptive mode with optional stress induction and a meditation-only mode.
This work presents EarStreAM, a closed-loop earable system built on OpenEarable 2.0 and a companion smartphone app that continuously monitors in-ear PPG to derive heart rate and heart rate variability as stress proxies, triggers an LLM-generated personalized guided meditation that adapts in real time to the user's physiological state, and terminates it once stress returns to baseline, demonstrated in two modes: a biosignal-adaptive mode with optional stress induction and a meditation-only mode.
This work presents EarStreAM, a closed-loop earable system built on OpenEarable 2.0 and a companion smartphone app that continuously monitors in-ear PPG to derive heart rate and heart rate variability as stress proxies, triggers an LLM-generated personalized guided meditation that adapts in real time to the user's physiological state, and terminates it once stress returns to baseline, demonstrated in two modes: a biosignal-adaptive mode with optional stress induction and a meditation-only mode.