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Erschienen in:
A Novel Bagging Ensemble Approach for Variable Ranking and Selection for Linear Regression Models Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Bio-Visual Fusion for Person-Independent Recognition of Pain Intensity

verfasst von : Markus Kächele, Philipp Werner, Ayoub Al-Hamadi, Günther Palm, Steffen Walter, Friedhelm Schwenker

Erschienen in: Multiple Classifier Systems

Verlag: Springer International Publishing

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Abstract

In this work, multi-modal fusion of video and biopotential signals is used to recognize pain in a person-independent scenario. For this purpose, participants were subjected to painful heat stimuli under controlled conditions. Subsequently, a multitude of features have been extracted from the available modalities. Experimental validation suggests that the cues that allow the successful recognition of pain are highly similar across different people and complementary in the analysed modalities to an extent that fusion methods are able to achieve an improvement over single modalities. Different fusion approaches (early, late, trainable) are compared on a large set of state-of-the art features for the biopotentials and video channels in multiple classification experiments.

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Vorheriges Kapitel An Empirical Investigation on the Use of Diversity for Creation of Classifier Ensembles
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Metadaten
Titel
Bio-Visual Fusion for Person-Independent Recognition of Pain Intensity
verfasst von
Markus Kächele
Philipp Werner
Ayoub Al-Hamadi
Günther Palm
Steffen Walter
Friedhelm Schwenker
Copyright-Jahr
2015
Buch
Multiple Classifier Systems

Print ISBN: 978-3-319-20247-1

Electronic ISBN: 978-3-319-20248-8

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-20248-8_19