Stress Detection in Computer Users From Keyboard and Mouse Dynamics

• Published in: IEEE transactions on consumer electronics Vol. 67; no. 1; pp. 12 - 19
• Main Authors: Pepa, Lucia, Sabatelli, Antonio, Ciabattoni, Lucio, Monteriu, Andrea, Lamberti, Fabrizio, Morra, Lia
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applications programs; Biomedical monitoring; Context; Field investigations; Hardware; in-the-wild study; keyboard; Keyboards; Machine learning; Mice; Monitoring; mouse; Mouse devices; Stress; Stress classification; System effectiveness; Task analysis
• ISSN: 0098-3063; 1558-4127
• DOI: 10.1109/TCE.2020.3045228

Detecting stress in computer users, while technically challenging, is of the utmost importance in the workplace, especially now that remote working scenarios are becoming ubiquitous. In this context, cost-effective, subject-independent systems are needed that can be embedded in consumer devices and classify users' stress in a reliable and unobtrusive fashion. Leveraging keyboard and mouse dynamics is particularly appealing in this context as it exploits readily available sensors. However, available studies are mostly performed in laboratory conditions, and there is a lack of on-field investigations in closer-to-real-world settings. In this study, keyboard and mouse data from 62 volunteers were experimentally collected in-the-wild using a purpose-built Web application, designed to induce stress by asking each subject to perform 8 computer tasks under different stressful conditions. The application of Multiple Instance Learning (MIL) to Random Forest (RF) classification allowed the devised system to successfully distinguish 3 stress-level classes from keyboard (76% accuracy) and mouse (63% accuracy) data. Classifiers were further evaluated via confusion matrix, precision, recall, and F1-score.