  The rapid growth of the availability of wearable biosensors has created the
opportunity for using biological signals to measure worker performance. An
important question is how to use such signals to not just measure, but actually
predict worker performance on a task under stressful and potentially high risk
conditions. Here we show that the biological signal known as galvanic skin
response (GSR) allows such a prediction. We conduct an experiment where
subjects answer arithmetic questions under low and high stress conditions while
having their GSR monitored using a wearable biosensor. Using only the GSR
measured under low stress conditions, we are able to predict which subjects
will perform well under high stress conditions, achieving an area under the
curve (AUC) of 0.76. If we try to make similar predictions without using any
biometric signals, the AUC barely exceeds 0.50. Our result suggests that
performance in high stress conditions can be predicted using signals obtained
from wearable biosensors in low stress conditions.
