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Erschienen in:
Improving Explanatory Power of Machine Learning in the Symbolic Data Analysis Framework Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Individual Finger Movement Recognition Based on sEMG and Classification Techniques

verfasst von : Laura Stella Vega-Escobar, Andrés Eduardo Castro-Ospina, Leonardo Duque-Muñoz

Erschienen in: Progress in Artificial Intelligence and Pattern Recognition

Verlag: Springer International Publishing

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Abstract

Hand gesture recognition is an active research area of human machine interfaces in which the person performs a hand gesture and a machine recognize the actual movement. However, the gestures can be seen as combination of individual finger movements, and recognizing the individual finger movements could improve the gesture recognition. This work presents a framework for finger movement recognition based on the feature extraction of the superficial electromiographic signals generated in the arm. We acquired a dataset with 54 subjects, and eight signals (channels) per subject. Then, features extracted in three types of domains were analized namely, time, frequency, and time-frequency forming a feature set of 720 features. A subset of features were selected and a support vector machine and k-NN classifiers were trained with a 10-fold cross-validation to prevent overfitting. We reached an accuracy over 90\(\%\) implying that our proposed framework facilitates the finger movement recognition.

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Vorheriges Kapitel Exploring Local Deep Representations for Facial Gender Classification in Videos
Nächstes Kapitel Watermarking Based on Krawtchouk Moments for Handwritten Document Images
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Metadaten
Titel
Individual Finger Movement Recognition Based on sEMG and Classification Techniques
verfasst von
Laura Stella Vega-Escobar
Andrés Eduardo Castro-Ospina
Leonardo Duque-Muñoz
Copyright-Jahr
2018
Buch
Progress in Artificial Intelligence and Pattern Recognition

Print ISBN: 978-3-030-01131-4

Electronic ISBN: 978-3-030-01132-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01132-1_13

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