Stress increases the risk of several mental and
physical health problems like anxiety, hypertension, and cardiovascular diseases. Better guidance and interventions towards
mitigating the impact of stress can be provided if stress can
be monitored continuously. The recent proliferation of wearable
devices and their capability in measuring several physiological
signals related to stress have created the opportunity to measure
stress continuously in the wild. Wearable devices used to measure
physiological signals are mostly placed on the wrist and the
chest. Though currently chest sensors, with/without wrist sensors,
provide better results in detecting stress than using wrist sensors
only, chest devices are not as convenient and prevalent as wrist
devices, particularly in the free-living context. In this paper, we
present a solution to detect stress using wrist sensors that emulate
the gold standard chest sensors. Data from wrist sensors are
translated into the data from chest sensors, and the translated
data is used for stress detection without requiring the users to
wear any device on the chest. We evaluated our solution using a
public dataset, and results show that our solution detects stress
with accuracy comparable to the gold standard chest devices
which are impractical for daily use
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An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work
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.
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- Award ID(s):
- 1704889
- PAR ID:
- 10168807
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 19
- Issue:
- 17
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 1-21
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s19173766
