Cargando…

A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network

Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Zhen, He, Changxin, Yang, Kuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412393/
https://www.ncbi.nlm.nih.gov/pubmed/32709164
http://dx.doi.org/10.3390/s20143994
_version_ 1783568597679865856
author Zhang, Zhen
He, Changxin
Yang, Kuo
author_facet Zhang, Zhen
He, Changxin
Yang, Kuo
author_sort Zhang, Zhen
collection PubMed
description Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach such as recurrent neural network (RNN) has provided a choice to automatically learn features from raw data. This paper presents a novel hand gesture prediction method by using an RNN model to learn from raw sEMG data and predict gestures. The sEMG signals of 21 short-term hand gestures of 13 subjects were recorded with a Myo armband, which is a non-intrusive, low cost, commercial portable device. At the start of the gesture, the trained model outputs an instantaneous prediction for the sEMG data. Experimental results showed that the more time steps of data that were known, the higher instantaneous prediction accuracy the proposed model gave. The predicted accuracy reached about 89.6% when the data of 40-time steps (200 ms) were used to predict hand gesture. This means that the gesture could be predicted with a delay of 200 ms after the hand starts to perform the gesture, instead of waiting for the end of the gesture.
format Online
Article
Text
id pubmed-7412393
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-74123932020-08-26 A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network Zhang, Zhen He, Changxin Yang, Kuo Sensors (Basel) Letter Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach such as recurrent neural network (RNN) has provided a choice to automatically learn features from raw data. This paper presents a novel hand gesture prediction method by using an RNN model to learn from raw sEMG data and predict gestures. The sEMG signals of 21 short-term hand gestures of 13 subjects were recorded with a Myo armband, which is a non-intrusive, low cost, commercial portable device. At the start of the gesture, the trained model outputs an instantaneous prediction for the sEMG data. Experimental results showed that the more time steps of data that were known, the higher instantaneous prediction accuracy the proposed model gave. The predicted accuracy reached about 89.6% when the data of 40-time steps (200 ms) were used to predict hand gesture. This means that the gesture could be predicted with a delay of 200 ms after the hand starts to perform the gesture, instead of waiting for the end of the gesture. MDPI 2020-07-17 /pmc/articles/PMC7412393/ /pubmed/32709164 http://dx.doi.org/10.3390/s20143994 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Letter
Zhang, Zhen
He, Changxin
Yang, Kuo
A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network
title A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network
title_full A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network
title_fullStr A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network
title_full_unstemmed A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network
title_short A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network
title_sort novel surface electromyographic signal-based hand gesture prediction using a recurrent neural network
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412393/
https://www.ncbi.nlm.nih.gov/pubmed/32709164
http://dx.doi.org/10.3390/s20143994
work_keys_str_mv AT zhangzhen anovelsurfaceelectromyographicsignalbasedhandgesturepredictionusingarecurrentneuralnetwork
AT hechangxin anovelsurfaceelectromyographicsignalbasedhandgesturepredictionusingarecurrentneuralnetwork
AT yangkuo anovelsurfaceelectromyographicsignalbasedhandgesturepredictionusingarecurrentneuralnetwork
AT zhangzhen novelsurfaceelectromyographicsignalbasedhandgesturepredictionusingarecurrentneuralnetwork
AT hechangxin novelsurfaceelectromyographicsignalbasedhandgesturepredictionusingarecurrentneuralnetwork
AT yangkuo novelsurfaceelectromyographicsignalbasedhandgesturepredictionusingarecurrentneuralnetwork