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EMG Feature Extraction Toolbox

version 1.1.2 (7.9 KB) by Jingwei Too
This toolbox offers 17 feature extraction methods (EMAV, EWL, MAV, WL, SSC, ZC ...) for Electromyography (EMG) signals applications.

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Updated 22 Jul 2020

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This toolbox offers 17 types of EMG features
(1) Enhanced Mean absolute value (EMAV)
(2) Enhanced Wavelength (EWL)
(3) Mean Absolute Value (MAV)
(4) Slope Sign Change (SSC)
(5) Zero Crossing (ZC)
(6) Waveform Length (WL)
(7) Root Mean Square (RMS)
(8) Average Amplitude Change (AAC)
(9) Difference Absolute Standard Deviation Value (DASDV)
(10) Log Detector (LD)
(11) Modified Mean Absolute Value (MMAV)
(12) Modified Mean Absolute Value 2 (MMAV2)
(13) Myopulse Percentage Rate (MYOP)
(14) Simple Square Integral (SSI)
(15) Variance of EMG (VAR)
(16) Willison Amplitude (WA)
(17) Maximum Fractal Length (MFL)

The "Main" demos how the feature extraction methods can be applied by using the generated sample signal.

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Detail of feature extraction methods can be found in the following papers:
[1] Too, J., Abdullah, A.R. and Saad, N.M., 2019. Classification of hand movements based on discrete wavelet transform and enhanced feature extraction. Int. J. Adv. Comput. Sci. Appl, 10(6), pp.83-89.
DOI: http://dx.doi.org/10.14569/IJACSA.2019.0100612

[2] Too, J., Abdullah, A.R., Mohd Saad, N. and Tee, W., 2019. EMG feature selection and classification using a Pbest-guide binary particle swarm optimization. Computation, 7(1), p.12.
DOI: https://doi.org/10.3390/computation7010012

Cite As

Too, Jingwei, et al. “Classification of Hand Movements Based on Discrete Wavelet Transform and Enhanced Feature Extraction.” International Journal of Advanced Computer Science and Applications, vol. 10, no. 6, The Science and Information Organization, 2019, doi:10.14569/ijacsa.2019.0100612.

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Too, Jingwei, et al. “EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization.” Computation, vol. 7, no. 1, MDPI AG, Feb. 2019, p. 12, doi:10.3390/computation7010012.

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Comments and Ratings (9)

Huang Jian

Thank you very much!

Reneira Seeamber

Thank you so much for this toolbox!

shuaiming

very good!

Jingwei Too

Dear Sai Krishna,

You may set the value of the threshold based on research articles. Normally, we test with several values like 0.001, 0.01, 0.1 .... and apply the optimal one.

Sai Krishna

What should be the values of threshold for EMG Data??

Jingwei Too

Dear Polo Joachín,

You need to read some related paper before you started. To use it, you need an EMG signal.
In the "main" file, you just replace the "X" with your EMG signal.

Polo Joachín

How do I use it. I am new.

Jingwei Too

Dear Marcus Schneider, Thank you for the information. I have updated the program.

Marcus Schneider

Unfortunately, the calculation of zero crossings and sign slope changes are wrong.

MATLAB Release Compatibility
Created with R2018a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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