Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time

The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has...

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Autores principales:	Zengyu Qing, Zongxing Lu, Yingjie Cai, Jing Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	surface electromyography machine learning gesture decoding muscle fatigue forearm angle acquisition time Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/68f077a40fa6419187399e1cec853ad9
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spelling	oai:doaj.org-article:68f077a40fa6419187399e1cec853ad92021-11-25T18:58:38ZElements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time10.3390/s212277131424-8220https://doaj.org/article/68f077a40fa6419187399e1cec853ad92021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7713https://doaj.org/toc/1424-8220The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has received a lot of research attention. In this paper, the effects of muscle fatigue, forearm angle and acquisition time on the accuracy of gesture decoding were researched. Taking 11 static gestures as samples, four specific muscles (i.e., superficial flexor digitorum (SFD), flexor carpi ulnaris (FCU), extensor carpi radialis longus (ECRL) and finger extensor (FE)) were selected to sample sEMG signals. Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Change (SSC) were chosen as signal eigenvalues; Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) were used to construct classification models, and finally, the decoding accuracies of the classification models were obtained under different influencing elements. The experimental results showed that the decoding accuracy of the classification model decreased by an average of 7%, 10%, and 13% considering muscle fatigue, forearm angle and acquisition time, respectively. Furthermore, the acquisition time had the biggest impact on decoding accuracy, with a maximum reduction of nearly 20%.Zengyu QingZongxing LuYingjie CaiJing WangMDPI AGarticlesurface electromyographymachine learninggesture decodingmuscle fatigueforearm angleacquisition timeChemical technologyTP1-1185ENSensors, Vol 21, Iss 7713, p 7713 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	surface electromyography machine learning gesture decoding muscle fatigue forearm angle acquisition time Chemical technology TP1-1185
spellingShingle	surface electromyography machine learning gesture decoding muscle fatigue forearm angle acquisition time Chemical technology TP1-1185 Zengyu Qing Zongxing Lu Yingjie Cai Jing Wang Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time
description	The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has received a lot of research attention. In this paper, the effects of muscle fatigue, forearm angle and acquisition time on the accuracy of gesture decoding were researched. Taking 11 static gestures as samples, four specific muscles (i.e., superficial flexor digitorum (SFD), flexor carpi ulnaris (FCU), extensor carpi radialis longus (ECRL) and finger extensor (FE)) were selected to sample sEMG signals. Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Change (SSC) were chosen as signal eigenvalues; Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) were used to construct classification models, and finally, the decoding accuracies of the classification models were obtained under different influencing elements. The experimental results showed that the decoding accuracy of the classification model decreased by an average of 7%, 10%, and 13% considering muscle fatigue, forearm angle and acquisition time, respectively. Furthermore, the acquisition time had the biggest impact on decoding accuracy, with a maximum reduction of nearly 20%.
format	article
author	Zengyu Qing Zongxing Lu Yingjie Cai Jing Wang
author_facet	Zengyu Qing Zongxing Lu Yingjie Cai Jing Wang
author_sort	Zengyu Qing
title	Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time
title_short	Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time
title_full	Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time
title_fullStr	Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time
title_full_unstemmed	Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time
title_sort	elements influencing semg-based gesture decoding: muscle fatigue, forearm angle and acquisition time
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/68f077a40fa6419187399e1cec853ad9
work_keys_str_mv	AT zengyuqing elementsinfluencingsemgbasedgesturedecodingmusclefatigueforearmangleandacquisitiontime AT zongxinglu elementsinfluencingsemgbasedgesturedecodingmusclefatigueforearmangleandacquisitiontime AT yingjiecai elementsinfluencingsemgbasedgesturedecodingmusclefatigueforearmangleandacquisitiontime AT jingwang elementsinfluencingsemgbasedgesturedecodingmusclefatigueforearmangleandacquisitiontime
_version_	1718410466100772864

Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time

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