Hybrid fusion approach for detecting affects from multichannel physiology

Authors:
Md. Sazzad Hussain;Rafael A. Calvo;Payam Aghaei Pour
Affiliations:
National ICT Australia, Australian Technology Park, Eveleigh, Australia and School of Electrical and Information Engineering, University of Sydney, Australia;School of Electrical and Information Engineering, University of Sydney, Australia;School of Electrical and Information Engineering, University of Sydney, Australia
Venue:
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Year:
2011

Citing 10
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Abstract

Bringing emotional intelligence to computer interfaces is one of the primary goals of affective computing. This goal requires detecting emotions often through multichannel physiology and/or behavioral modalities. While most affective computing studies report high affect detection rate from physiological data, there is no consensus on which methodology in terms of feature selection or classification works best for this type of data. This study presents a framework for fusing physiological features from multiple channels using machine learning techniques to improve the accuracy of affect detection. A hybrid fusion based on weighted majority vote technique for integrating decisions from individual channels and feature level fusion is proposed. The results show that decision fusion can achieve higher classification accuracy for affect detection compared to the individual channels and feature level fusion. However, the highest performance is achieved using the hybrid fusion model.