Article
A Study on the Classification Effect of sEMG Signals in
Different Vibration Environments Based on the LDA Algorithm
Yanchao Wang
1
, Ye Tian
1,2,
*, Jinying Zhu
2
, Haotian She
1
, Hiroshi Yokoi
2,3
, Yinlai Jiang
2,3
and Qiang Huang
1,2
Citation: Wang, Y.; Tian, Y.; Zhu, J.;
She, H.; Yokoi, H.; Jiang, Y.; Huang, Q.
A Study on the Classification Effect of
sEMG Signals in Different Vibration
Environments Based on the LDA
Algorithm. Sensors 2021, 21, 6234.
https://doi.org/10.3390/s21186234
Academic Editors: Andrea Mannini,
Clive D’Souza and Pietro Picerno
Received: 8 August 2021
Accepted: 14 September 2021
Published: 17 September 2021
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4.0/).
1
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China;
3120170102@bit.edu.cn (Y.W.); 3120150094@bit.edu.cn (H.S.); qhuang@bit.edu.cn (Q.H.)
2
Beijing Advanced Innovation Center for Intelligent Robot and System, Beijing 100081, China;
jinyingzhu@bit.edu.cn (J.Z.); yokoi@mce.uec.ac.jp (H.Y.); jiang.yinlai@uec.ac.jp (Y.J.)
3
Graduate School of Informatics and Engineering, The University of Electro-Communications,
Tokyo 182-8585, Japan
* Correspondence: tianye7248@bit.edu.cn
Abstract:
Myoelectric prosthesis has become an important aid to disabled people. Although it can
help people to recover to a nearly normal life, whether they can adapt to severe working conditions
is a subject that is yet to be studied. Generally speaking, the working environment is dominated by
vibration. This paper takes the gripping action as its research object, and focuses on the identification
of grasping intentions under different vibration frequencies in different working conditions. In
this way, the possibility of the disabled people who wear myoelectric prosthesis to work in various
vibration environment is studied. In this paper, an experimental test platform capable of simulating
0–50 Hz vibration was established, and the Surface Electromyography (sEMG) signals of the human
arm in the open and grasping states were obtained through the MP160 physiological record analysis
system. Considering the reliability of human intention recognition and the rapidity of algorithm
processing, six different time-domain features and the Linear Discriminant Analysis (LDA) classifier
were selected as the sEMG signal feature extraction and recognition algorithms in this paper. When
two kinds of features, Zero Crossing (ZC) and
Root Mean Square (RMS)
, were used as input, the
accuracy of LDA algorithm can reach 96.9%. When three features, RMS, Minimum Value (MIN), and
Variance (VAR), were used as inputs, the accuracy of the LDA algorithm can reach 98.0%. When the
six features were used as inputs, the accuracy of the LDA algorithm reached 98.4%. In the analysis of
different vibration frequencies, it was found that when the vibration frequency reached 20 Hz, the
average accuracy of the LDA algorithm in recognizing actions was low, while at 0 Hz, 40 Hz and
50 Hz, the average accuracy was relatively high. This is of great significance in guiding disabled
people to work in a vibration environment in the future.
Keywords:
surface EMG signal; feature extraction; LDA algorithm; hand-motion recognition;
vibration frequency
1. Introduction
The hand is an important functional and motor organ of the human body. The inability
to work due to upper limb motor dysfunction and limb deformity caused by various
accidents imposes a great burden on the lives of patients, making them unable to work and
earn their own living. The common method is to install artificial limbs to help them recover
their hand function. In the current application of artificial limbs, myoelectric prosthesis [
1
]
is the most widely used, which is characterized by strong sense of control and convenient
use. Although artificial limbs can help people recover to a nearly normal life, whether they
can adapt to severe working conditions is a subject that is yet to be studied. Generally
speaking, the working environment is dominated by vibration. Therefore, this paper takes
the gripping action as the research object, and focuses on the identification of grasping
intentions under different vibration frequencies in different working conditions, so as to
Sensors 2021, 21, 6234. https://doi.org/10.3390/s21186234 https://www.mdpi.com/journal/sensors
