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

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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 ( 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:
# Toshiharu Kishi graduted from Chiba University at March 2009.


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.