Stress Recognition in Daily Work

• Published in: Pervasive Computing Paradigms for Mental Health Vol. 604; pp. 23 - 33
• Main Authors: Nakashima, Yoshiki, Kim, Jonghwa, Flutura, Simon, Seiderer, Andreas, André, Elisabeth
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Communications in Computer and Information Science
• Subjects: Activity recognition; Machine learning; Medical equipment & techniques; Multi-modality; Stress
• ISBN: 3319322699; 9783319322698
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-319-32270-4_3

Automatic detection of work-related stress has attracted an increasing amount of attention from researchers from various disciplines and industries. An experiment is discussed in this paper that was designed to evaluate the efficacy of multimodal sensor measures that have often been used but not yet been systematically tested and compared with each other in previous work, such as pressure distribution sensor, physiological sensors, and an eye tracker. We used the Stroop test and information pick up task as the stressors. In the subject independent case in particular, signals from the combined (chair and floor) pressure distribution sensors, which we consider the most feasible sensors in the office environment, resulted in higher recognition accuracy rates than the physiological or eye tracker signals for the two stressors.