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Real-time physiological data collection and analysis play a central role inmodern well-being applications. Personalized classifiers and detectors havebeen shown to outperform general classifiers in many contexts. However,building effective personalized classifiers in everyday settings - as opposedto controlled settings - necessitates the online collection of a labeleddataset by interacting with the user. This need leads to several challenges,ranging from building an effective system for the collection of the signals andlabels, to developing strategies to interact with the user and building adataset that represents the many user contexts that occur in daily life. Basedon a stress detection use case, this paper (1) builds a system for thereal-time collection and analysis of photoplethysmogram, acceleration,gyroscope, and gravity data from a wearable sensor, as well as self-reportedstress labels based on Ecological Momentary Assessment (EMA), and (2) collectsand analyzes a dataset to extract statistics of users' response to queries andthe quality of the collected signals as a function of the context, here definedas the user's activity and the time of the day.

Data Collection and Labeling of Real-Time IoT-Enabled Bio-Signals in Everyday Settings for Mental Health Improvement