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2019 | OriginalPaper | Buchkapitel

Evolutionary Algorithms for the Design of Neural Network Classifiers for the Classification of Pain Intensity

verfasst von : Danila Mamontov, Iana Polonskaia, Alina Skorokhod, Eugene Semenkin, Viktor Kessler, Friedhelm Schwenker

Erschienen in: Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

Verlag: Springer International Publishing

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Abstract

In this paper we present a study on multi-modal pain intensity recognition based on video and bio-physiological sensor data. The newly recorded SenseEmotion dataset consisting of 40 individuals, each subjected to three gradually increasing levels of painful heat stimuli, has been used for the evaluation of the proposed algorithms. We propose and evaluated evolutionary algorithms for the design and adaptation of the structure of deep artificial neural network architectures. Feedforward Neural Network and Recurrent Neural Network have been considered for the optimisation by using a Self-Configuring Genetic Algorithm (SelfCGA) and Self-Configuring Genetic Programming (SelfCGP).

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Titel: Evolutionary Algorithms for the Design of Neural Network Classifiers for the Classification of Pain Intensity
verfasst von: Danila Mamontov
Iana Polonskaia
Alina Skorokhod
Eugene Semenkin
Viktor Kessler
Friedhelm Schwenker
Verlag: Springer International Publishing
Buch: Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
Print ISBN: 978-3-030-20983-4

Electronic ISBN: 978-3-030-20984-1

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-20984-1_8

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