  Objective: A person's affective state has known relationships to
physiological processes which can be measured by wearable sensors. However,
while there are general trends those relationships can be person-specific. This
work proposes using neural processes as a way to address individual
differences.
  Methods: Stress classifiers built from classic machine learning models and
from neural processes are compared on two datasets using
leave-one-participant-out cross-validation. The neural processes models are
contextualized on data from a brief period of a particular person's recording.
  Results: The neural processes models outperformed the standard machine
learning models, and had the best performance when using periods of stress and
baseline as context. Contextual points chosen from other participants led to
lower performance.
  Conclusion: Neural processes can learn to adapt to person-specific
physiological sensor data. There are a wide range of affective and medical
applications for which this model could prove useful.
