Multimodal person independent recognition of workload related biosignal patterns
| Autor: | Felix Putze, Dominic Heger, Tanja Schultz, Jan Jarvis |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Majority rule
medicine.diagnostic_test Computer science business.industry Speech recognition Feature vector Driving simulator Pattern recognition Workload Electroencephalography Linear discriminant analysis ComputingMethodologies_PATTERNRECOGNITION medicine Artificial intelligence Biosignal business Classifier (UML) |
| Zdroj: | ICMI |
| DOI: | 10.1145/2070481.2070516 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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