Abstract
In this study, we demonstrate that we can use non-invasive physiology sensing to detect stress level and lying, within the context of structured poker game settings. In our study, we instrument players with non-invasive physiological sensors that can measure heart rate, temperature, skin conductance, movement, and audio (voice/speech) during live play in real-money, no-limit Hold'em poker tournaments. We show how simply derived physiological features such as voice pitch variation, skin conductance peaks, and heart rate variability are correlated to a number of high-stress situations found in these type of tournaments. Using these features, we can develop simple classifiers that can accurately identify stress and bluffing in these settings.
