Predicting Performance Under Stressful Conditions Using Galvanic Skin Response (1606.01836v1)

Abstract: 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.