Predicting performance using galvanic skin response
Name
960806938-MIT.pdf
Description
Full printable version
Size
5.93 MB
Format
Adobe PDF
Checksum (MD5)
70715a8628da637ae5a29429791f7187
Author(s)
Mundell, Lee Carter
Advisor(s)
Tauhid Zaman and Juan Pablo Vielma.
Alternative Title
Predicting performance using GSR
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
The rapid growth of the availability of wearable biosensors has created the opportunity for using physiological signals to measure worker performance. An important question is how to use such signals to not just measure, but actually predict worker performance on a task under stressful and potentially high risk conditions. Here we show that the biological signal known as galvanic skin response (GSR) allows such a prediction. We conduct an experiment where subjects answer arithmetic questions under low and high stress conditions while having their GSR monitored. Using only the GSR measured under low stress conditions, we are able to predict which subjects will perform well under high stress conditions with a median accuracy of 75%. If we try to make similar predictions without using any biometric signals, the median accuracy is 50%. Our results suggest that performance in high stress conditions can be predicted using signals obtained from GSR sensors in low stress conditions.
Description
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 49-52).
Subjects
Operations Research Center.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
Terms of Use
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