Automatic Stress Recognition in Real-Life Settings

Technologies to automatically recognize stress, are extremely important to prevent chronic psychological stress and the pathophysiological risks associated to it. The introduction of comfortable and wearable biosensors have created new opportunities to measure stress in real-life environments, but there is often great variability in how people experience stress and how they express it physiologically. In this project, we modify the loss function of Support Vector Machines to encode a person's tendency to feel more or less stressed, and give more importance to the training samples of the most similar subjects. These changes are validated in a case study where skin conductance was monitored in nine call center employees during one week of their regular work. Employees working in this type of settings usually handle high volumes of calls every day, and they frequently interact with angry and frustrated customers that lead to high stress levels.