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Vol. 32, No. 8(2), S&M2292

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Online: ISSN 2435-0869
Sensors and Materials
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Sensors and Materials, Volume 32, Number 9(2) (2020)
Copyright(C) MYU K.K.
pp. 2981-2998
S&M2317 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2878
Published in advance: June 13, 2020
Published: September 18, 2020

Acoustic-sensing-based Gesture Recognition Using Hierarchical Classifier [PDF]

Miki Kawato and Kaori Fujinami

(Received March 23, 2020; Accepted May 25, 2020)

Keywords: gesture recognition, acoustic sensing, machine learning, hierarchical classifier, feature engineering

A gestural input to control artifacts and access the digital world is an essential part of highly usable systems. In this article, we propose a gesture recognition method that leverages the sound generated by the friction between a surface such as a table and a finger or pen, in which 17 different gestures are defined. The gesture recognition process is regarded as a 17-class classification problem; 89 classification features are defined to represent the envelope of each input sound, while a hierarchical classifier structure is employed to increase the accuracy of confusable classes. Offline experiments show that the highest accuracy is 0.954 under a condition where the classifiers are customized for each user, while an accuracy of 0.854 is obtained under a condition where the classifiers are trained without using the data from test users. We also confirm the effectiveness of the hierarchical classifier approach compared with a single-flat-classifier approach and that of a feature engineering approach compared with a feature learning approach. The information of individual features is also presented.

Corresponding author: Kaori Fujinami


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Cite this article
Miki Kawato and Kaori Fujinami, Acoustic-sensing-based Gesture Recognition Using Hierarchical Classifier, Sens. Mater., Vol. 32, No. 9, 2020, p. 2981-2998.



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