Remote and Independent Detection of Human Stress Using Sweat and AI

Previous studies exploring human sweat ducts as biological antennas in the sub-THz range have shown that the electromagnetic (EM) response of the skin is modulated by the persons mental and physical stress. These findings naturally raised hopes of a new remote avenue for detecting human stress. However, as those studies unmasked stress using correlations with well-established markers such as the Galvanic Skin Response (GSR), the question of whether the EM response could serve as an independent marker of stress remained unanswered. Here, we provide a positive answer to this question by showing that machine learning models trained on EM reflections from 21 participants, subjected to physical and mental stress, were able to estimate the presence of stress in a signal from a new participant, in a matter of seconds, with above 90% accuracy.