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

Timm Hörmann, Marc Hesse, Peter Christ, Michael Adams, Christian Menßen, Ulrich Rückert

2016

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.

Paper Citation

in Harvard Style

Hörmann T., Hesse M., Christ P., Adams M., Menßen C. and Rückert U. (2016). Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 42-51. DOI: 10.5220/0005665000420051

in Bibtex Style

@conference{biosignals16,
author={Timm Hörmann and Marc Hesse and Peter Christ and Michael Adams and Christian Menßen and Ulrich Rückert},
title={Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)},
year={2016},
pages={42-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005665000420051},
isbn={978-989-758-170-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)
TI - Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters
SN - 978-989-758-170-0
AU - Hörmann T.
AU - Hesse M.
AU - Christ P.
AU - Adams M.
AU - Menßen C.
AU - Rückert U.
PY - 2016
SP - 42
EP - 51
DO - 10.5220/0005665000420051