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Comparative Study
. 2019 Aug 30;19(17):3766.
doi: 10.3390/s19173766.

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work

Fatema Akbar  1 , Gloria Mark  2 , Ioannis Pavlidis  3 , Ricardo Gutierrez-Osuna  4
Affiliations
Comparative Study

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work

Fatema Akbar et al. Sensors (Basel). .

Abstract

Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use.

Keywords: ECG; EDA; PPG; human–computer interaction; physiology; stress; thermal imaging; unobtrusive sensors; wearables.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Experiment phases (CWT: Stroop Color-Word test).
Figure 2
Figure 2
EDA signal during the five sessions for two participants. The x-axis is cut at 400 s, thus only showing the first 400 s of the DT.
Figure 3
Figure 3
Wrist.HR signal during the five sessions for two participants. The x-axis is cut at 400 s, thus only showing the first 400 s of the DT.
Figure 4
Figure 4
An example of a participant’s BR data showing degrading signal in the presentation session. The x-axis is cut at 400 s, thus only showing the first 400 s of the DT.
Figure 5
Figure 5
An example of a participant’s chest.HR data where increased stress is captured during stressful tasks.
Figure 6
Figure 6
An example of a participant’s data with overlapping high frequency responses in the baseline session, likely due to sensor friction and detachment from the skin.
Figure 7
Figure 7
Example of a participant with PP signal that captures increased stress in stressful sessions. This participant took the color-word test and received emails in batches in DT (monotasking). This example also shows instances of missing data during the presentation session.
Figure 8
Figure 8
Example of a participant with the PP signal that captures increased stress in stressful sessions. This participant watched the relaxing video and received emails continually in DT (multitasking).
Figure 9
Figure 9
Boxplot of the ratios of missing PP data for all participants per session.
See this image and copyright information in PMC

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