Computer-based Stress Measurement

Stress is a major risk factor for the development of physical and mental illnesses, including hypertension, depression and anxiety disorders.
To study stress and its effects, psychologists use classical stress induction paradigms (stress tests) such as the Trier Social Stress Test (TSST), which are cost- and time-intensive. As scalable solutions to induce stress are still missing, we have developed a Digital Stress Test (DST).
Previous methods for the (early) detection of acute stress reactions mostly focus on measuring subjective stress or individual parameters of the physiological reaction, such as heart rate.
In various studies, we record the non-verbal behaviour of test persons together with hormonal, cardiovascular (e.g. heart rate) and subjective markers in form of questionnaires.
Based on these multimodal data, we develop algorithms for automatic stress detection and validate them in natural environments.
In the long term, we hope to be able to objectively and individually measure and feedback acute stress in an everyday setting and thus enable better handling of individual stressors.

This research is funded by the "Empathic Artificial Intelligence" (EKI) grant, FKZ 01IS20046.