Phasor represented EMG feature extraction against varying contraction level of prosthetic control

Küçük Resim Yok

Tarih

2020

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Elsevier Sci Ltd

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

This paper introduces phasor representation of electromyography (EMG) feature extraction (PRE). The well-known EMG signal analysis methods, namely root mean square (RMS), and waveform length (WL) are adopted into phasor form depending electrode placement. The values of these methods are computed from 8-channel EMG signals, and their magnitudes with respect to origin are used to construct phasor represented features in this study. The class separability of the PRE is strengthened by adding difference EMG and Euclidean distanced phasor in order to obtain improved feature set against force and electrode variations. The simulations (three schemes) are performed on publicly available EMG dataset on transradial amputees, and the results are presented in terms of accuracy and processing time considering the control strategies of a prosthetic hand. Linear (LDA), and quadratic (QDA) discriminant analysis, and knearest neighbor (k-NN) classifiers are trained, and tested by the PRE features. Our method outperforms previous accuracy rates in some cases, and reaches to accuracy results of the first study using this dataset without using any reduction method. In our simulations, accuracy rates up to 71.17% (PRE with QDA) for six classes hand movements with three force levels are obtained decreasing processing time by 81.83%. (C) 2020 Elsevier Ltd. All rights reserved.

Açıklama

Mert, Ahmet/0000-0003-4236-3646

Anahtar Kelimeler

Electromyography, Pattern recognition, Prosthetic hand control, Myoelectric control, Transradial amputees

Kaynak

Biomedical Signal Processing And Control

WoS Q Değeri

Q2

Scopus Q Değeri

Q1

Cilt

59

Sayı

Künye