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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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) |
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surface electromyography machine learning gesture decoding muscle fatigue forearm angle acquisition time Chemical technology TP1-1185 |
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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 |
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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 |
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_version_ |
1718410466100772864 |