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A Human-Centered Behavioral Informatics Read chapter Read first chapter

2019 | OriginalPaper | Chapter

16. Kendon Model-Based Gesture Recognition Using Hidden Markov Model and Learning Vector Quantization

Authors : Domenico De Felice, Francesco Camastra

Published in: Quantifying and Processing Biomedical and Behavioral Signals

Publisher: Springer International Publishing

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Abstract

The paper presents a dynamic gesture recognizer, that assumes that the gesture can be described by Kendon Gesture model. The gesture recognizer has four modules. The first module performs the feature extaction, using the skeleton representation of the body person provided by NITE library of Kinect. The second module, formed by Learning Vector Quantization, has the task of individuating the initial and the final handposes of the gesture, i.e., when the gesture starts and terminates. The third unit performs the dimensionality reduction. The last module, formed by a discrete Hidden Markov, perfoms the gesture classification. The proposed recognizer compares favourably, in terms of accuracy, most of existing dynamic gesture recognizers.

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previous chapter Evolution Characterization of Alzheimer’s Disease Using eLORETA’s Three-Dimensional Distribution of the Current Density and Small-World Network
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Footnotes
1
Recent trackers, prefers to measure orientation by quaternions, instead of usual Euler angles, since Euler angle representation can be affected by the gymbel lock.
 
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Metadata
Title
Kendon Model-Based Gesture Recognition Using Hidden Markov Model and Learning Vector Quantization
Authors
Domenico De Felice
Francesco Camastra
Copyright Year
2019
Book
Quantifying and Processing Biomedical and Behavioral Signals

Print ISBN: 978-3-319-95094-5

Electronic ISBN: 978-3-319-95095-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-95095-2_16

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