Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition

Joint Authors

Liu, Xiaoyun
Xi, Xugang
Hua, Xian
Wang, Hujiao
Zhang, Wei

Source

Journal of Healthcare Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-11-24

Country of Publication

Egypt

No. of Pages

11

Main Topic

Public Health
Medicine

Abstract EN

The feature extraction of surface electromyography (sEMG) signals has been an important aspect of myoelectric prosthesis control.

To improve the practicability of myoelectric prosthetic hands, we proposed a feature extraction method for sEMG signals that uses wavelet weighted permutation entropy (WWPE).

First, wavelet transform was used to decompose and preprocess sEMG signals collected from the relevant muscles of the upper limbs to obtain the wavelet sub-bands in each frequency segment.

Then, the weighted permutation entropies (WPEs) of the wavelet sub-bands were extracted to construct WWPE feature set.

Lastly, the WWPE feature set was used as input to a support vector machine (SVM) classifier and a backpropagation neural network (BPNN) classifier to recognize seven hand movements.

Experimental results show that the proposed method exhibits remarkable recognition accuracy that is superior to those of single sub-band feature set and commonly used time-domain feature set.

The maximum recognition accuracy rate is 100% for hand movements, and the average recognition accuracy rates of SVM and BPNN are 100% and 98%, respectively.

American Psychological Association (APA)

Liu, Xiaoyun& Xi, Xugang& Hua, Xian& Wang, Hujiao& Zhang, Wei. 2020. Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1186450

Modern Language Association (MLA)

Liu, Xiaoyun…[et al.]. Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition. Journal of Healthcare Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1186450

American Medical Association (AMA)

Liu, Xiaoyun& Xi, Xugang& Hua, Xian& Wang, Hujiao& Zhang, Wei. Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1186450

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1186450