Predicting user action from skin conductance

Authors:
László Laufer;Bottyán Németh
Affiliations:
Budapest University of Technology and Economics, Bp. Egry J.u.1.E.ép;Budapest University of Technology and Economics, Bp. Magyar Tudósok Krt. 2
Venue:
Proceedings of the 13th international conference on Intelligent user interfaces
Year:
2008

Citing 3
Cited 3

Neural networks: a systematic introduction

Neural networks: a systematic introduction
Affective Computing for HCI

Proceedings of HCI International (the 8th International Conference on Human-Computer Interaction) on Human-Computer Interaction: Ergonomics and User Interfaces-Volume I - Volume I
A continuous and objective evaluation of emotional experience with interactive play environments

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Usability Evaluation by Monitoring Physiological and Other Data Simultaneously with a Time-Resolution of Only a Few Seconds

Proceedings of the 13th International Conference on Human-Computer Interaction. Part I: New Trends
Designers of Different Cognitive Styles Editing E-Learning Materials Studied by Monitoring Physiological and Other Data Simultaneously

EPCE '09 Proceedings of the 8th International Conference on Engineering Psychology and Cognitive Ergonomics: Held as Part of HCI International 2009
Exploring passive user interaction for adaptive narratives

Proceedings of the 2012 ACM international conference on Intelligent User Interfaces

Quantified Score

Hi-index	0.00

Visualization

Abstract

There are many studies focusing on enhancing physiological data in user interfaces. On one hand biofeedback games are using skin conductance and heart rate data to reflect the emotional state of the user, on the other hand BCI research tries to conclude user intentions from EEG signals. In our research we are collecting usual biofeedback data but process it with complex algorithms similarly to the BCI methodologies. This way we are able to conclude more complex user states than relaxation or anxiety. In our experiments we asked users to play with a simple arcade game, while we were recording physiological data. We were training artificial neural networks to learn the time of user action from the physiological signals. The networks were capable of detecting and also predicting user action 2 seconds before it was carried out.