Feasibility study on the application of a spiking neural network in myoelectric control systems
In recent years, the effectiveness of a spiking neural network (SNN) for Electromyography (EMG) pattern recognition has been validated, but there is a lack of comprehensive consideration of the problems of heavy training burden, poor robustness, and high energy consumption in the application of actu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1174760/full |
| _version_ | 1797806302279237632 |
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| author | Antong Sun Xiang Chen Mengjuan Xu Xu Zhang Xun Chen |
| author_facet | Antong Sun Xiang Chen Mengjuan Xu Xu Zhang Xun Chen |
| author_sort | Antong Sun |
| collection | DOAJ |
| description | In recent years, the effectiveness of a spiking neural network (SNN) for Electromyography (EMG) pattern recognition has been validated, but there is a lack of comprehensive consideration of the problems of heavy training burden, poor robustness, and high energy consumption in the application of actual myoelectric control systems. In order to explore the feasibility of the application of SNN in actual myoelectric control systems, this paper investigated an EMG pattern recognition scheme based on SNN. To alleviate the differences in EMG distribution caused by electrode shifts and individual differences, the adaptive threshold encoding was applied to gesture sample encoding. To improve the feature extraction ability of SNN, the leaky-integrate-and-fire (LIF) neuron that combines voltage–current effect was adopted as a spike neuron model. To balance recognition accuracy and power consumption, experiments were designed to determine encoding parameter and LIF neuron release threshold. By conducting the gesture recognition experiments considering different training test ratios, electrode shifts, and user independences on the nine-gesture high-density and low-density EMG datasets respectively, the advantages of the proposed SNN-based scheme have been verified. Compared with a Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM) and Linear Discriminant Analysis (LDA), SNN can effectively reduce the number of repetitions in the training set, and its power consumption was reduced by 1–2 orders of magnitude. For the high-density and low-density EMG datasets, SNN improved the overall average accuracies by about (0.99 ~ 14.91%) under different training test ratios. For the high-density EMG dataset, the accuracy of SNN was improved by (0.94 ~ 13.76%) under electrode-shift condition and (3.81 ~ 18.95%) in user-independent case. The advantages of SNN in alleviating the user training burden, reducing power consumption, and improving robustness are of great significance for the implementation of user-friendly low-power myoelectric control systems. |
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| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-13T06:05:17Z |
| publishDate | 2023-06-01 |
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| series | Frontiers in Neuroscience |
| spelling | doaj.art-64ae3370e3ba47fd863c80484b5aacf72023-06-12T04:24:44ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-06-011710.3389/fnins.2023.11747601174760Feasibility study on the application of a spiking neural network in myoelectric control systemsAntong SunXiang ChenMengjuan XuXu ZhangXun ChenIn recent years, the effectiveness of a spiking neural network (SNN) for Electromyography (EMG) pattern recognition has been validated, but there is a lack of comprehensive consideration of the problems of heavy training burden, poor robustness, and high energy consumption in the application of actual myoelectric control systems. In order to explore the feasibility of the application of SNN in actual myoelectric control systems, this paper investigated an EMG pattern recognition scheme based on SNN. To alleviate the differences in EMG distribution caused by electrode shifts and individual differences, the adaptive threshold encoding was applied to gesture sample encoding. To improve the feature extraction ability of SNN, the leaky-integrate-and-fire (LIF) neuron that combines voltage–current effect was adopted as a spike neuron model. To balance recognition accuracy and power consumption, experiments were designed to determine encoding parameter and LIF neuron release threshold. By conducting the gesture recognition experiments considering different training test ratios, electrode shifts, and user independences on the nine-gesture high-density and low-density EMG datasets respectively, the advantages of the proposed SNN-based scheme have been verified. Compared with a Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM) and Linear Discriminant Analysis (LDA), SNN can effectively reduce the number of repetitions in the training set, and its power consumption was reduced by 1–2 orders of magnitude. For the high-density and low-density EMG datasets, SNN improved the overall average accuracies by about (0.99 ~ 14.91%) under different training test ratios. For the high-density EMG dataset, the accuracy of SNN was improved by (0.94 ~ 13.76%) under electrode-shift condition and (3.81 ~ 18.95%) in user-independent case. The advantages of SNN in alleviating the user training burden, reducing power consumption, and improving robustness are of great significance for the implementation of user-friendly low-power myoelectric control systems.https://www.frontiersin.org/articles/10.3389/fnins.2023.1174760/fullgesture recognitionelectromyographyspike encodingLIFSNN |
| spellingShingle | Antong Sun Xiang Chen Mengjuan Xu Xu Zhang Xun Chen Feasibility study on the application of a spiking neural network in myoelectric control systems Frontiers in Neuroscience gesture recognition electromyography spike encoding LIF SNN |
| title | Feasibility study on the application of a spiking neural network in myoelectric control systems |
| title_full | Feasibility study on the application of a spiking neural network in myoelectric control systems |
| title_fullStr | Feasibility study on the application of a spiking neural network in myoelectric control systems |
| title_full_unstemmed | Feasibility study on the application of a spiking neural network in myoelectric control systems |
| title_short | Feasibility study on the application of a spiking neural network in myoelectric control systems |
| title_sort | feasibility study on the application of a spiking neural network in myoelectric control systems |
| topic | gesture recognition electromyography spike encoding LIF SNN |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1174760/full |
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