Article

Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters

Authors:

Ulrich RückertAuthors Info & Claims

BIOSTEC 2016: Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies

Pages 42 - 51

https://doi.org/10.5220/0005665000420051

Published: 21 February 2016 Publication History

Abstract

In this paper we present a method to predict cognitive workload during the interaction with a tablet computer. To set up a predictor that estimates the reflected self-reported cognitive workload we analyzed the information gain of heart rate, electrodermal activity and user input (touch) based features. From the derived optimal feature set we present a Gaussian Process based learner that enables fine-grained and short term detection of cognitive workload. Average inter-subject accuracy in 10-fold cross validation is 74.1 % for the fine-grained 5-class problem and 96.0 % for the binary class problem.

Cited By

View all

Zhou JArshad SLuo SYu KBerkovsky SChen FMark GFussell SLampe Cschraefel mHourcade JAppert CWigdor D(2017)Indexing Cognitive Load using Blood Volume Pulse FeaturesProceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems10.1145/3027063.3053140(2269-2275)Online publication date: 6-May-2017
https://dl.acm.org/doi/10.1145/3027063.3053140
Luo SZhou JDuh HChen FMark GFussell SLampe Cschraefel mHourcade JAppert CWigdor D(2017)BVP Feature Signal Analysis for Intelligent User InterfaceProceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems10.1145/3027063.3053121(1861-1868)Online publication date: 6-May-2017
https://dl.acm.org/doi/10.1145/3027063.3053121

Index Terms

Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

Recommendations

Cognitive Workload Quantified by Physiological Sensors in Realistic Immersive Settings
Augmented Cognition
Abstract
Cognitive workload changes have been studied and utilized as a means of assessment for engagement and learner’s performance during training. Yet, it is unclear how varying levels of simulator immersion affect learner cognitive workload. Wearable ...
Classifying driver workload using physiological and driving performance data: two field studies
CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Understanding the driver's cognitive load is important for evaluating in-vehicle user interfaces. This paper describes experiments to assess machine learning classification algorithms on their ability to automatically identify elevated cognitive ...
Use of wearable physiological sensors to predict cognitive workload in a visuospatial learning task
BACKGROUND:
Increased cognitive workload, sometimes known as mental strain or mental effort, has been associated with reduced performance.
OBJECTIVE:
The use of physiological monitoring was ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

BIOSTEC 2016: Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies

February 2016

1642 pages

ISBN:9789897581700

Editors:
James Gilbert
University of Hull
,
Haim Azhari
Technion-Israel Institute of Technology
,
Hesham Ali
University of Nebraska at Omaha
,
Carla Quintão
FCT-UNL
,
Jan Sliwa
Bern University of Applied Sciences
,
Carolina Ruiz
WPI
,
Ana Fred
Instituto de Telecomunicações / IST
,
Hugo Gamboa
LIBPHYS-UNL / FCT - New University of Lisbon

Publisher

SCITEPRESS - Science and Technology Publications, Lda

Setubal, Portugal

Publication History

Published: 21 February 2016

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zhou JArshad SLuo SYu KBerkovsky SChen FMark GFussell SLampe Cschraefel mHourcade JAppert CWigdor D(2017)Indexing Cognitive Load using Blood Volume Pulse FeaturesProceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems10.1145/3027063.3053140(2269-2275)Online publication date: 6-May-2017
https://dl.acm.org/doi/10.1145/3027063.3053140
Luo SZhou JDuh HChen FMark GFussell SLampe Cschraefel mHourcade JAppert CWigdor D(2017)BVP Feature Signal Analysis for Intelligent User InterfaceProceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems10.1145/3027063.3053121(1861-1868)Online publication date: 6-May-2017
https://dl.acm.org/doi/10.1145/3027063.3053121

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

Cited By

Index Terms

Recommendations

Cognitive Workload Quantified by Physiological Sensors in Realistic Immersive Settings

Classifying driver workload using physiological and driving performance data: two field studies

Use of wearable physiological sensors to predict cognitive workload in a visuospatial learning task

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations