  Since stress contributes to a broad range of mental and physical health
problems, the objective assessment of stress is essential for behavioral and
physiological studies. Although several studies have evaluated stress levels in
controlled settings, objective stress assessment in everyday settings is still
largely under-explored due to challenges arising from confounding contextual
factors and limited adherence for self-reports. In this paper, we explore the
objective prediction of stress levels in everyday settings based on heart rate
(HR) and heart rate variability (HRV) captured via low-cost and easy-to-wear
photoplethysmography (PPG) sensors that are widely available on newer smart
wearable devices. We present a layered system architecture for personalized
stress monitoring that supports a tunable collection of data samples for
labeling, and present a method for selecting informative samples from the
stream of real-time data for labeling. We captured the stress levels of
fourteen volunteers through self-reported questionnaires over periods of
between 1-3 months, and explored binary stress detection based on HR and HRV
using Machine Learning Methods. We observe promising preliminary results given
that the dataset is collected in the challenging environments of everyday
settings. The binary stress detector is fairly accurate and can detect
stressful vs non-stressful samples with a macro-F1 score of up to \%76. Our
study lays the groundwork for more sophisticated labeling strategies that
generate context-aware, personalized models that will empower health
professionals to provide personalized interventions.
