Young Researcher Paper Award 2023
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Print: ISSN 0914-4935
Online: ISSN 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.
Online: ISSN 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.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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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
Template
English
Publisher
MYU K.K.
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)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 32, Number 3(2) (2020)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 1005-1013
S&M2155 Research Paper of Special Issue https://doi.org/10.18494/SAM.2020.2634 Published in advance: February 7, 2020 Published: March 19, 2020 Detection of Head Motion from Facial Feature Points Using Deep Learning for Tele-operation of Robot [PDF] Masahiko Minamoto, Shigeki Hori, Hideyuki Kobayashi, Toshihiro Kawase, Tetsuro Miyazaki, Takahiro Kanno, and Kenji Kawashima (Received September 27, 2019; Accepted October 28, 2019) Keywords: visual interface, tele-operation, deep learning, laparoscope holder
We propose an interface for the tele-operation of a laparoscope-holder robot via head
movement using facial feature point detection. Fourteen feature points on the operator’s face
are detected using a camera. The vertical and horizontal rotation angles and the distance
between the face and the camera are estimated from the points using deep learning. The
training data for deep learning are obtained using a dummy face. The root-mean-square error
(RMSE) between the estimated and directly measured values is calculated for different numbers
of nodes, layers, and epochs, and suitable numbers are determined from the RMSE values.
The trained data are evaluated with four subjects. The effectiveness of the proposed method is
demonstrated experimentally.
Corresponding author: Masahiko Minamoto  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Masahiko Minamoto, Shigeki Hori, Hideyuki Kobayashi, Toshihiro Kawase, Tetsuro Miyazaki, Takahiro Kanno, and Kenji Kawashima, Detection of Head Motion from Facial Feature Points Using Deep Learning for Tele-operation of Robot, Sens. Mater., Vol. 32, No. 3, 2020, p. 1005-1013. |
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