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
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Lenguaje:EN
Publicado: MDPI AG 2021
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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
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