Challenge
Three out of ten employees in Switzerland experience critical levels of stress at the workplace. Chronic stress can have detrimental effects on our health. The question arises whether new technologies such as artificial intelligence (AI) can help to detect stress.
Approach
To this aim, we designed a stress detection system based on machine learning (ML). The project was conducted in collaboration with international researchers from the fields of psychology, data science and computer science.
Stress detection with ML:
Detection of stress levels from data sources available in office environments (i.e., mouse movements, keystroke dynamics, cardiac activity) with the help of ML.
Ethical aspects of digital health:
Investigation of employees’ value-related concerns and wishes for a digital stress management intervention (dSMI) to shape its development, design and deployment at the workplace.
Results
Stress detection with ML: detection of self-reported stress from mouse movements, keystroke dynamics and cardiac activity collected in a lab experiment (Naegelin et al 2023). Currently, we are validating the results from the lab experiment in a real office environment, developing an ML pipeline capable of detecting self-reported stress from mouse movements, keystroke dynamics and cardiac activity based on real-life office and home office data
Ethical aspects of digital health: an online study revealed that intention to use a digital stress management intervention at the workplace were moderate to high (Kerr et al. 2023). Employees’ concerns included worries that an intervention would not be effective or even amplify their stress levels. Privacy and accountability concerns were higher if the intervention including artificial intelligence.
Lead Researchers
Dr. Jasmine Kerr, Dr. Mara Nägelin, Dr. Raphaël Weibel
Project Status
Completed
