Powered by smartphones and wearable devices, the Experience Sampling Method (ESM) has increased in popularity for studying behaviors, thoughts, and experiences over time and in situ. Participants in ESM studies receive several notifications a day to self-report but often disengage due to intrusive and poorly timed notifications. Consequently, the response rate drops over time, hampering data collection and degrading ecological validity. Researchers have experimented with various strategies to optimize notification scheduling, including personalization, context sensing, and machine learning (ML). Edge computing can facilitate the training of ML models without the need for server communications, which is especially convenient for in-the-wild studies with unreliable network connectivity. Complementary logical evaluations on edge devices can minimize participant burden by accounting for sampling density, i.e., ensuring a minimum number of well-distributed daily notifications. However, these efforts raise engineering and scientific challenges related to avoiding cold start and training models on smartwatches. To overcome these challenges, we propose an open-source architecture and software that facilitates online learning to optimize notification delivery. Our feasibility study with \(N=37\) participants resulted in a response rate of 10.2% higher and a reaction time of 9.6% lower on average compared to the classical interval-based sampling.