  Because stress is subjective and is expressed differently from one person to
another, generic stress prediction models (i.e., models that predict the stress
of any person) perform crudely. Only person-specific ones (i.e., models that
predict the stress of a preordained person) yield reliable predictions, but
they are not adaptable and costly to deploy in real-world environments. For
illustration, in an office environment, a stress monitoring system that uses
person-specific models would require collecting new data and training a new
model for every employee. Moreover, once deployed, the models would deteriorate
and need expensive periodic upgrades because stress is dynamic and depends on
unforeseeable factors. We propose a simple, yet practical and cost effective
calibration technique that derives an accurate and personalized stress
prediction model from physiological samples collected from a large population.
We validate our approach on two stress datasets. The results show that our
technique performs much better than a generic model. For instance, a generic
model achieved only a 42.5% accuracy. However, with only 100 calibration
samples, we raised its accuracy to 95.2% We also propose a blueprint for a
stress monitoring system based on our strategy, and we debate its merits and
limitation. Finally, we made public our source code and the relevant datasets
to allow other researchers to replicate our findings.
