Title
A Sensor-Enabled Digital Trier Social Stress Test In An Enterprise Context
Abstract
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.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8857779
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Computer vision,Trier social stress test,Task analysis,State-Trait Anxiety Inventory,Support vector machine classifier,Computer science,Photoplethysmogram,Ground truth,Artificial intelligence,Cognition,Applied psychology,Skin conductance
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
12