@inproceedings{jarvis_multimodal_2011,
	address = {New York, NY, USA},
	series = {{ICMI} '11},
	title = {Multimodal {Person} {Independent} {Recognition} of {Workload} {Related} {Biosignal} {Patterns}},
	isbn = {978-1-4503-0641-6},
	url = {http://doi.acm.org/10.1145/2070481.2070516},
	doi = {10.1145/2070481.2070516},
	abstract = {This paper presents an online multimodal person independent workload classification system using blood volume pressure, respiration measures, electrodermal activity and electroencephalography. For each modality a classifier based on linear discriminant analysis is trained. The classification results obtained on short data frames are fused using weighted majority voting. The system was trained and evaluated on a large training corpus of 152 participants, exposed to controlled and uncontrolled scenarios for inducing workload, including a driving task conducted in a realistic driving simulator. Using person dependent feature space normalization, we achieve a classification accuracy of up to 94\% for discrimination of relaxed state vs. high workload.},
	urldate = {2014-06-05TZ},
	booktitle = {Proceedings of the 13th {International} {Conference} on {Multimodal} {Interfaces}},
	publisher = {ACM},
	author = {Jarvis, Jan and Putze, Felix and Heger, Dominic and Schultz, Tanja},
	year = {2011},
	pages = {205--208}
}

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