{"_id":"rtw3AL8vmPRSC7NqE","bibbaseid":"jarvis-putze-heger-schultz-multimodalpersonindependentrecognitionofworkloadrelatedbiosignalpatterns-2011","downloads":0,"creationDate":"2016-10-05T13:48:43.109Z","title":"Multimodal Person Independent Recognition of Workload Related Biosignal Patterns","author_short":["Jarvis, J.","Putze, F.","Heger, D.","Schultz, T."],"year":2011,"bibtype":"inproceedings","biburl":"http://bibbase.org/zotero/alanlivio","bibdata":{"bibtype":"inproceedings","type":"inproceedings","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":[{"propositions":[],"lastnames":["Jarvis"],"firstnames":["Jan"],"suffixes":[]},{"propositions":[],"lastnames":["Putze"],"firstnames":["Felix"],"suffixes":[]},{"propositions":[],"lastnames":["Heger"],"firstnames":["Dominic"],"suffixes":[]},{"propositions":[],"lastnames":["Schultz"],"firstnames":["Tanja"],"suffixes":[]}],"year":"2011","pages":"205--208","bibtex":"@inproceedings{jarvis_multimodal_2011,\n\taddress = {New York, NY, USA},\n\tseries = {{ICMI} '11},\n\ttitle = {Multimodal {Person} {Independent} {Recognition} of {Workload} {Related} {Biosignal} {Patterns}},\n\tisbn = {978-1-4503-0641-6},\n\turl = {http://doi.acm.org/10.1145/2070481.2070516},\n\tdoi = {10.1145/2070481.2070516},\n\tabstract = {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.},\n\turldate = {2014-06-05TZ},\n\tbooktitle = {Proceedings of the 13th {International} {Conference} on {Multimodal} {Interfaces}},\n\tpublisher = {ACM},\n\tauthor = {Jarvis, Jan and Putze, Felix and Heger, Dominic and Schultz, Tanja},\n\tyear = {2011},\n\tpages = {205--208}\n}\n\n","author_short":["Jarvis, J.","Putze, F.","Heger, D.","Schultz, T."],"key":"jarvis_multimodal_2011","id":"jarvis_multimodal_2011","bibbaseid":"jarvis-putze-heger-schultz-multimodalpersonindependentrecognitionofworkloadrelatedbiosignalpatterns-2011","role":"author","urls":{"Paper":"http://doi.acm.org/10.1145/2070481.2070516"},"downloads":0},"search_terms":["multimodal","person","independent","recognition","workload","related","biosignal","patterns","jarvis","putze","heger","schultz"],"keywords":[],"authorIDs":[],"dataSources":["tudya6YojbqEiF783"]}