A Sensor-enabled Digital Trier Social Stress Test in an Enterprise Context

A Sensor-enabled Digital Trier Social Stress Test in an Enterprise Context

The Trier Social Stress Test (TSST) protocol is a widely accepted method of inducing social and/or cognitive stress in participants and studying its effects. Traditionally, this protocol is administered in laboratory or university settings, which are less formal than in offices. In this paper, we re...

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Bibliographic Details
Published in:2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2019; pp. 1321 - 1325
Main Authors: Gavas, Rahul D, Das, Deepan, Bhattacharjee, Tanuka, Sheshachala, Mithun B, Hissaria, Lalit K, Vempada, Ramu R, Viraraghavan, Venkata S, Choudhury, Anirban D, Muralidharan, Kartik, Ramakrishnan, Ramesh K, Balamuralidhar, P, Pal, Arpan
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01-07-2019
Subjects:
Anxiety
Atmospheric measurements
Exercise Test
Galvanic Skin Response
Particle measurements
Protocols
Sensors
Skin
Stress
Stress, Psychological
Task analysis
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Summary:The Trier Social Stress Test (TSST) protocol is a widely accepted method of inducing social and/or cognitive stress in participants and studying its effects. Traditionally, this protocol is administered in laboratory or university settings, which are less formal than in offices. In this paper, we report the results of the analysis of multi-modal sensor data collected from employees of an enterprise who underwent the test. We briefly discuss the adaptations that enabled administering it digitally in a semi-automatic mode with minimal researcher/test-administrator intervention. In our setup, noninvasive sensor-signals, including the Galvanic Skin Response and Photoplethysmogram, were collected during and outside the stress-inducing tasks. We analyze the data collected from twenty participants and show that the State Trait Anxiety Inventory (STAI) score is needed in assessing the effect of the digital version of the TSST. A support vector machine classifier yielded an F 1 score of 0.723 with the STAI score taken as ground truth. We also introduce the idea of ground truth based on the change in the STAI scores to reduce variation due to subjective interpretation, for which an F 1 score of 0.847 was obtained.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2019.8857779