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

Hörmann T, Hesse M, Christ P, Adams M, Menßen C, Rückert U (2016)
In: Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies. 4. SCITEPRESS: 42-51.

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Conference Paper | Published | English
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.
Publishing Year
Conference
9th International Joint Conference on Biomedical Engineering Systems and Technologies
Location
Rome, Italy
Conference Date
2016-02-21 – 2016-02-23
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Hörmann T, Hesse M, Christ P, Adams M, Menßen C, Rückert U. 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. Vol 4. SCITEPRESS; 2016: 42-51.
Hörmann, T., Hesse, M., Christ, P., Adams, M., Menßen, C., & Rückert, U. (2016). Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters. Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, 4, 42-51.
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, vol. 4, (SCITEPRESS), 42-51.
Hörmann, T., et al., 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. no.4 SCITEPRESS, pp. 42-51.
T. Hörmann, et al., “Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters”, Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, vol. 4, SCITEPRESS, 2016, pp.42-51.
Hörmann, T., Hesse, M., Christ, P., Adams, M., Menßen, C., Rückert, U.: Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters. Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies. 4, p. 42-51. SCITEPRESS (2016).
Hörmann, Timm, Hesse, Marc, Christ, Peter, Adams, Michael, Menßen, Christian, and Rückert, Ulrich. “Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters”. Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies. SCITEPRESS, 2016.Vol. 4. 42-51.
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