Advancing the Understanding and Measurement of Workplace Stress in Remote Information Workers from Passive Sensors and Behavioral Data - Morshed, Mehrab Bin; Hernandez, Javier; McDuff, Daniel; Suh, Jina; Howe, Esther; Rowan, Kael; Abdin, Marah I; Ramos, Gonzalo; Tran, Tracy; Czerwinski, Mary (International Conference on Affective Computing & Intelligent Interaction (ACII 2022), 2022)
Workplace stress has been increasing during the recent decades and has worsened by the unique demands imposed by COVID-19 and the new remote/hybrid work settings. High-stress working conditions can be detrimental to the health and wellness of workers and can lead to significant business costs in terms of productivity loss and medical costs. An important step towards managing stress involves finding comfortable ways to sense workers as well as recognizing stress as soon as it happens. This work explores the potential value of using pervasive sensors such as keyboards and webcams as well as behavioral data such as calendar and e-mail activity to passively assess individual stress levels of works in real-life. In particular, we collected a large corpus of such data from 46 remote information workers over one month and asked them to self-report their stress levels and other relevant factors several times a day. Analysis of the data demonstrates that passive sensors can effectively detect both triggers and manifestations of workplace stress and that having access to prior data of the worker is critical towards developing well-performing stress recognition models. Furthermore, we provide qualitative feedback capturing workers’ preferences in the context of workplace stress monitoring