Exploration of Scenario-based Simulations for Stress Benchmarking in Swiss Public Service

Abstract

Statistical interpretation of stress-related indicators collected through wearable biosensors often relies on benchmarking, especially in the context of stress management interventions. However, it remains unclear how to construct stress level benchmarks for group stress-related indicators using limited historical data. This study examines whether the method of numerical simulation of stress-related responses could contribute to constructing benchmark curves. Experimental data consists of physiological and non-physiological signals of 18 Swiss public servants collected through wearable biosensors. This study draws upon Stress Pattern Recognition algorithm and Markov Chain modeling for simulating emotional responses according to specified data-driven scenarios of high and low stress. Proposed method allows constructing benchmark curves for an Overarousal Index. Results demonstrate that numerical simulation based on small datasets can be used effectively for constructing stress level benchmarks. The findings contribute to methodological knowledge in statistical learning on Stress Pattern Recognition algorithms and Markov Chains modeling by expanding their application to a new field of emotional response simulation according to scenarios.

Longitudinal estimation of stress-related states through bio-sensor data

“…The approach used in this study is comparable in longitudinal estimation steps to those used by other researchers. The present research reflects Study 3 [4] and Study 4 [5] involving three estimation steps: identification, measurement and visualization. The evaluation framework is similar to that found in Study 5 [3] and Study 6 [6] involving four estimation steps.…”

“…However, previous research has not clearly established the accuracy of measurements based on indirect measurements through unsupervised algorithms [3][4][5][6] and has not formalized the process of longitudinal evaluation of stress-related states.…”

“…In particular, validation is not always performed based on remote stress identification [4,5]. However, longitudinal evaluation of stress-related states using bio-sensor data requires validated measurements.…”

“…The measurement of stress-related signals can be direct [18,19] or indirect [3][4][5][6]. Direct measurement is usually conducted through isolated signals, while the stress level can be determined based on the level of variable(s) associated with stress.…”

“…Other researchers used indirect measurement with ML algorithms. Some indirect algorithm-based measures follow a fully unsupervised approach [4,5] that does not include validation. Two recent studies include implied IW validation [3,6].…”