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2018 | OriginalPaper | Buchkapitel

Identifying an Emotional State from Body Movements Using Genetic-Based Algorithms

verfasst von : Yann Maret, Daniel Oberson, Marina Gavrilova

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

Emotions may not only be perceived by humans, but could also be identified and recognized by a machine. Emotion recognition through pattern analysis can be used in expert systems, lie detectors, medical emergencies, as well as during rescue operations to quickly identify people in distress. This paper describes a system capable of recognizing emotions based on the arm movement. Features extracted from 3D skeleton using Kinect sensor are classified by five commonly used machine learning techniques: K nearest neighbors, SVM, Decision tree, Neural Network and Naive Bayes. A genetic algorithm is then invoked to find the best system parameters to achieve the higher recognition rate. The system achieved 98.96% average accuracy on the experimental dataset.

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Vorheriges Kapitel Deriving Functions for Pareto Optimal Fronts Using Genetic Programming

Nächstes Kapitel Particle Swarm Optimization with Single Particle Repulsivity for Multi-modal Optimization

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Titel: Identifying an Emotional State from Body Movements Using Genetic-Based Algorithms
verfasst von: Yann Maret
Daniel Oberson
Marina Gavrilova
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Soft Computing
Print ISBN: 978-3-319-91252-3

Electronic ISBN: 978-3-319-91253-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-91253-0_44

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