Recurrent Neural Network as Estimator for a Virtual sEMG Channel

Juliano C Machado; Vinicius H Cene; Alexandre Balbinot

doi:10.1109/EMBC.2019.8857462

Recurrent Neural Network as Estimator for a Virtual sEMG Channel

Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul:2019:3620-3623. doi: 10.1109/EMBC.2019.8857462.

Authors

Juliano C Machado, Vinicius H Cene, Alexandre Balbinot

PMID: 31946660
DOI: 10.1109/EMBC.2019.8857462

Abstract

This study aims at estimating a virtual surface Electromyography (sEMG) channel through a Recurrent Neural Network (RNN) by using Long Short-Term Memory (LSTM) nodes. The virtual channel is used to classify hand postures from the publicly NinaPro database with a multi-class, one-against-all Support Vector Machine (SVM) using the Root Mean Square RMS of the sEMG signal as feature. The classification of the signals through the virtual channel was compared with uncontaminated data and data contaminated with noise saturation. The hit rate from the clean data has averaged 73.96% ± 3.02%. The classification from the contaminated data of one of the channels has improved from 9.29% ± 4.42% to 66.48% ± 6.11% with the virtual channel.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Databases, Factual
Electromyography*
Hand
Humans
Neural Networks, Computer*
Support Vector Machine*