In Search of Harmful Stress

Human body produces different physiological stress reaction when you hit a toe to a doorstep than when you panic at a job interview. The impact for body’s homeostasis varies depending on the reaction type and some reactions are harmful to our health. Currently, stress estimation is focused on binary identification between stress and non-stress stages. More detailed separation of stress reaction types is needed for detecting harmful stress. In this study, the Extreme Gradient Boosting algorithm was used to classify a baseline condition and physiological and psychosocial stress, based on psychophysiological signals monitored using a wrist sensor device. Classification was robust in separating the two stress states from baseline and from each other. The results provide support for novel approaches utilizing fine-grained estimation of stress type from wearable sensor data.