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

Topics: Detection and Identification; Medical Signal Acquisition, Analysis and Processing; Monitoring and Telemetry; Pattern Recognition; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics; Wearable Sensors and Systems

Authors: Timm Hörmann ; Marc Hesse ; Peter Christ ; Michael Adams ; Christian Menßen and Ulrich Rückert

Affiliation: CITEC and Bielefeld University, Germany

ISBN: 978-989-758-170-0

Keyword(s): Fine-Grained, Cognitive Workload, Stress, Heart Rate, Electrodermal Activity, Tablet Computer, Human Machine Interaction, Industry 4.0.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Detection and Identification ; Devices ; Health Engineering and Technology Applications ; Health Information Systems ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Monitoring and Telemetry ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics ; Sensor Networks ; Soft Computing ; Wearable Sensors and Systems

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.

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Paper citation in several formats:
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

@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},
}

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

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