Bibliographic Metadata
Bibliographic Metadata
- TitleFine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters
- Author
- Published
- LanguageEnglish
- Document typeConference Proceedings
- Keywords
- ISBN978-989-758-170-0
- URN
- DOI
Restriction-Information
- The document is publicly available on the WWW
Links
- Social MediaShare
- ReferenceNo Reference available
- IIIF
Files
Classification
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.
Stats
- The PDF-Document has been downloaded 5 times.