TY  - BOOK
AB  - 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.
DA  - 2016
DO  - 10.5220/0005665000420051
KW  - Fine-Grained
KW  - Cognitive Workload
KW  - Stress
KW  - Heart Rate
KW  - Electrodermal Activity
KW  - Tablet Computer
KW  - Human Machine Interaction
KW  - Industry 4.0.
LA  - eng
PY  - 2016
SE  - 42-51
SN  - 978-989-758-170-0
T2  - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies
TI  - Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29032484
Y2  - 2024-11-22T09:43:45
ER  -