Grasping force estimation for prosthetic hands via feature extraction of surface EMG

DOI

10.2991/aest-16.2016.23How to use a DOI?

Keywords

force estimation; feature extraction; surface EMG; prosthetic hands.

Abstract

A prosthetic hand with a self-regulated grip force could achieve different operation modes, which can help the upper limb amputees to fetch objects of different shapes. To get the appropriate grasping force with smaller samples and shorter training time, the method of threshold value judgment in this paper is effective on achieving the estimate of the discrete force basing on the mean absolute value (MAV) of EMG's level. The 10 subjects can be divided into 8 grasping patterns determined through three levels: the small, medium and great of grasping forces in experiments. Experimental results conditioned on small training samples and short training time show that the accuracy of force estimation is 72.91±9.58% and thereby convincing the effectiveness and reality of the proposed method.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lisha Xu
AU  - Gaoke Zhu
AU  - Xiaogang Duan
PY  - 2016/11
DA  - 2016/11
TI  - Grasping force estimation for prosthetic hands via feature extraction of surface EMG
BT  - Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
PB  - Atlantis Press
SP  - 177
EP  - 185
SN  - 1951-6851
UR  - https://doi.org/10.2991/aest-16.2016.23
DO  - 10.2991/aest-16.2016.23
ID  - Xu2016/11
ER  -