Name
Title | ||
|---|---|---|
Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters. |
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5220/0005665000420051 | BIOSIGNALS |
Field | DocType | Citations |
Data mining,Computer science,Information gain,Feature set,Cognitive workload,Gaussian process,Artificial intelligence,Industry 4.0,Cross-validation,Machine learning,Binary number,Human machine interaction | Conference | 2 |
PageRank | References | Authors |
0.45 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Timm Hormann | 1 | 8 | 2.38 |
| Marc Hesse | 2 | 10 | 4.47 |
| peter christ | 3 | 2 | 1.46 |
| Michael Adams | 4 | 21 | 6.52 |
| Christian Menßen | 5 | 2 | 0.45 |
| U. Rückert | 6 | 755 | 103.61 |