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Research Article Open Access
In this paper, we demonstrate that we can use non-invasive physiology sensing to detect stress and lying, within the context of Artificial Neural Network. We show how simply derived non-invasive physiological features such as voice pitch variation, and heart rate variability are correlated to a number of high stress situations found in real life. Using these features, we can develop simple linear models that can be used to identify stress and bluffing.