Journal of Robotics, Networking and Artificial Life

In Press, Uncorrected Proof, Available Online: 20 May 2020

Verification of a Combination of Gestures Accurately Recognized by Myo using Learning Curves

Authors
Kengo Kitakura, Hideyuki Tanaka*
Graduate School of Education, Hiroshima University, Kagamiyama 1-1-1, Higashi-hiroshima, Hiroshima 739-8524, Japan
*Corresponding author. Email: tanakalpha@hiroshima-u.ac.jp
Corresponding Author
Hideyuki Tanaka
Received 31 October 2019, Accepted 25 February 2020, Available Online 20 May 2020.
DOI
https://doi.org/10.2991/jrnal.k.200512.009How to use a DOI?
Keywords
Learning curve, data distribution, Myo armband, American sign, reliable gesture recognition
Abstract

This paper studies verification of a combination of hand gestures recognized by using the Myo armband as an input device. To this end, relationship between data distribution and learning curves is investigated for binary classification and multi-class classification problems. A verification method is then proposed for finding a combination of gestures accurately classified. Experiments show effectiveness of the proposed method.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Publication Date
2020/05
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
https://doi.org/10.2991/jrnal.k.200512.009How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Kengo Kitakura
AU  - Hideyuki Tanaka
PY  - 2020
DA  - 2020/05
TI  - Verification of a Combination of Gestures Accurately Recognized by Myo using Learning Curves
JO  - Journal of Robotics, Networking and Artificial Life
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.200512.009
DO  - https://doi.org/10.2991/jrnal.k.200512.009
ID  - Kitakura2020
ER  -