Journal of Software Engineering and Applications, 2019, 12, 383-392
https://www.scirp.org/journal/jsea
ISSN Online: 1945-3124
ISSN Print: 1945-3116
DOI:
10.4236/jsea.2019.129023 Sep. 29, 2019 383 Journal of Software Engineering and Applications
Hand Gestures Recognition Based on
One-Channel Surface EMG Signal
Junyi Cao
1
*, Zhongming Tian
2
*, Zhengtao Wang
3
*
#
1
Xi’an Jiao Tong University, Xi’an, China
2
Chongqing University, Chongqing, China
3
Sun Yat-sen University, Guangzhou, China
Abstract
This paper presents an experiment using OPENBCI to collect data of two
hand gestures and decoding the signal to distinguish gestures. The signal was
extracted with three electrodes on the subject’s forearm and transferred in
one channel. After utilizing a Butterworth bandpass filter, we chose a novel
way to detect gesture action segment. Instead of using moving average algo-
rithm, which is based on the calculation of energy, We developed an algo-
rithm based on the Hilbert transform to find a dynamic threshold and identi-
fied the action segment. Four features have been extracted from each activity
section, generating feature vectors for classification. During the process of
classification, we made a comparison between K-nearest-neighbors (KNN)
and support vector machine (SVM), based on a relatively small amount of
samples. Most common experiments are based on a large quantity of data to
pursue a highly fitted
model. But there are certain circumstances where we
cannot obtain enough training data, so it makes the exploration of best me-
thod to do classification under small sample data imperative. Though KNN is
known for its simplicity and practicability, it is a relatively time-consuming
method. On the other hand, SVM has a better performance in terms of time
requirement and recognition accuracy, due to its application of
different Risk
Minimization Principle. Experimental results show an average recognition
rate for the SVM algorithm that is 1.25% higher than for KNN while SVM is
2.031 s shorter than that KNN.
Keywords
Electromyography (EMG), Gesture Recognition, Hilbert Transform,
K-Nearest-Neighbors (KNN), Support Vector Machine (SVM)
*Three authors (listed according to alphabetical order of last name) contributed to this work equally.
How to cite this paper:
Cao, J.Y.
, Tian,
Z.
M. and Wang, Z.T. (2019)
Hand Gestures
Recognition Based on One
-Channel Sur-
face EMG Signal
.
Journal of Software E
n-
gineering and Applications
,
12
, 383-392.
https://doi.org/10.4236/jsea.2019.129023
Received:
August 18, 2019
Accepted:
September 26, 2019
Published:
September 29, 2019
Copyright © 201
9 by author(s) and
Scientific Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution
-NonCommercial
International License (
CC BY-NC 4.0).
http://creativecommons.org/licenses/by
-nc/4.0/
Open Access
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