RESEARCH ARTICLE


Finger Motion Classification by Forearm Skin Surface Vibration Signals



Wenwei Yu*, 1, Toshiharu Kishi#, 1, U. Rajendra Acharya2, Yuse Horiuchi1, Jose Gonzalez1
1 Department of Medical System Engineering, Graduate School of Engineering, Chiba University, Chiba, Japan
2 Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore

Article Information


Identifiers and Pagination:

Year: 2010
Volume: 4
First Page: 31
Last Page: 40
Publisher Id: TOMINFOJ-4-31
DOI: 10.2174/1874431101004020031

Article History:

Received Date: 8/10/2009
Revision Received Date: 25/11/2009
Acceptance Date: 25/11/2009
Electronic publication date: 28/5/2010
Collection year: 2010

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© Yu et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Medical System Engineering, Graduate School of Engineering, Chiba University, Japan; Tel +81-43-290-3231; Fax +81-43-290-3231; E-mail: yuwill@faculty.chiba-u.jp
# Toshiharu Kishi graduted from Chiba University at March 2009.


Abstract

The development of prosthetic hand systems with both decoration and motion functionality for hand amputees has attracted wide research interests. Motion-related myoelectric potentials measured from the surface of upper part of forearms were mostly employed to construct the interface between amputees and prosthesis.

However, finger motions, which play a major role in dexterous hand activities, could not be recognized from surface EMG (Electromyogram) signals.

The basic idea of this study is to use motion-related surface vibration, to detect independent finger motions without using EMG signals. In this research, accelerometers were used in a finger tapping experiment to collect the finger motion related mechanical vibration patterns. Since the basic properties of the signals are unknown, a norm based, a correlation coefficient based, and a power spectrum based method were applied to the signals for feature extraction. The extracted features were then fed to back-propagation neural networks to classify for different finger motions.

The results showed that, the finger motion identification is possible by using the neural networks to recognize vibration patterns.

Keywords: Finger motion detection, prosthetic application, skin surface vibration, neural network.