Stress Pattern Recognition Through Wearable Biosensors in the Workplace: Experimental Longitudinal Study on the Role of Motion Intensity

Abstract

Stress is a current issue in the workplace, manifesting itself through both psychological and physiological reactions. Biosensors might improve stress monitoring in the workplace, when employees become wearable device users. Yet, it remains unclear how to identify stress patterns through biosensors without direct observation of the users' activities. In particular, non-physiological aspects of employee activities altering physiological reactions, such as motion activity, may also be associated with stress measures. This longitudinal experimental study examines remote stress identification by testing whether a non-physiological signal of physical activity may improve the classification of stress-related physiological data collected through biosensors. The participants are 18 employees from Public Administration sector wearing biometric devices for around two months in the workplace. This study investigates the stress-related data classification, using established physiological measures (Galvanic Skin Response and Heart Rate) combined with a new non-physiological measure, associated with the user's physical activity (Motion Activity). Stress-related patterns are explored through unsupervised learning approach with help of Gaussian Mixture Model and K-Means classification analysis, completed by the bootstrap confidence intervals for evaluating uncertainty of classification. The results demonstrate that complementing physiological signals with a non-physiological signal, such as a physical activity-related information, improves stress pattern recognition through detection of emotional overarousal, arousal, and relaxation. These findings are especially promising in the context of the use of wearable devices for stress management, when stress-monitoring is done remotely and user' activity is not directly observed during measurements. Further research and cross-validation procedures should be used for building stress-identification algorithms for remote stress monitoring that include physiological and nonphysiological signals. Better understanding of stress measures may enhance the quality of stress management data collection processes through Information Systems, involved in the use of wearable devices in the workplace, and strengthen the data governance.

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

“…Moreover, the proposed evaluation framework uses emotional arousal and relaxation indicators, diverging from the extant literature except for Study 4 [5], drawing upon an index of arousal. However, our results on stress indicators are more consistent with a recent study using a theoretically justified model for identifying three stress-related states [23].…”

“…The first stage corresponds to the construction of a stress identification model from IW data. The Yerkes-Dodson law [20] and catastrophe theory of arousal [21,22] may underpin clustering-based stress pattern recognition and subsequent analytic data labeling [23]. The Yerkes-Dodson law assumes the existence of three ranges of arousal: (1) low range of arousal, (2) middle range of arousal and (3) high range of arousal.…”

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

“…Figure 2 shows a mixture distribution of path . In line with previous classification analysis [17], R is a most frequent state of individuals. Changes in the density of R episodes correspond to the changes in emotional states of users.…”

“…Present study on remote stress pattern recognition aims to expand methodological knowledge on data pre-processing under conditions of limited longitudinal data availability [17]. Current research follows several steps.…”

“…We identify clusters corresponding to the state of R, A and O. Next, we label input features accordingly, in line with previous research [17].…”