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

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English    日本語

Instructions for manuscript preparation
English    日本語


 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.

MYU Research

(proofreading and recording)

(translation service)

The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Sensors and Materials, Volume 32, Number 9(2) (2020)
Copyright(C) MYU K.K.
pp. 2981-2998
S&M2317 Research Paper of Special Issue
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

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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.

Forthcoming Regular Issues

Forthcoming Special Issues

Special Issue on International Conference on BioSensors, BioElectronics, BioMedical Devices, BioMEMS/NEMS and Applications 2019 (Bio4Apps 2019) (1)
Guest editor, Hirofumi Nogami and Masaya Miyazaki (Kyushu University)
Conference website

Special Issue on Optical Sensors: Novel Materials, Approaches, and Applications
Guest editor, Yap Wing Fen (Universiti Putra Malaysia)

Special Issue on Intelligent Sensing Control Analysis, Optimization, and Automation (2)
Guest editor, Cheng-Chi Wang (National Chin-Yi University of Technology)

Special Issue on Geomatics Technologies for the Realization of Smart Cities (2)
Guest editor, He Huang and XiangLei Liu (Beijing University of Civil Engineering and Architecture)

Special Issue on Cyber–Physical Systems (CPS) and Internet of Things (IoT)
Guest editor, Yutaka Arakawa (Kyushu University)

Special Issue on Sensors and Materials Emerging Investigators in Japan
Guest editor, Tsuyoshi Minami (The University of Tokyo)

Copyright(C) MYU K.K. All Rights Reserved.