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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11377)


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).


Multimodal pain intensity recognition Evolutionary algorithm Neural network 



The reported study was funded by Krasnoyarsk Regional Fund of Science according to the participation in the internship Recurrent neural Networks, Deep Learning for Video retrieval. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research. The work of FS was support by the SenseEmotion project funded by the Federal Ministry of Education and Research (BMBF).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Reshetnev Siberian State University of Science and TechnologyKrasnoyarskRussia
  2. 2.Institute of Neural Information ProcessingUlm UniversityUlmGermany

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