Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter Scheme
Convolutional neural network (CNN)-based models are widely used in human movement decoding based on surface electromyography. However, they capture only the spatial information of the surface electromyography and lack prior knowledge of the system, resulting in unsatisfactory decoding accuracy. To a...
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10082987/ |
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| author | Anyuan Zhang Qi Li Zhenlan Li Jiming Li |
| author_facet | Anyuan Zhang Qi Li Zhenlan Li Jiming Li |
| author_sort | Anyuan Zhang |
| collection | DOAJ |
| description | Convolutional neural network (CNN)-based models are widely used in human movement decoding based on surface electromyography. However, they capture only the spatial information of the surface electromyography and lack prior knowledge of the system, resulting in unsatisfactory decoding accuracy. To address these issues, we propose an attention-based Kalman filter scheme (AKFS), which uses an attention-based CNN model to better extract temporal information and a KF to add prior knowledge of the system. We further solve the problem of insufficient data due to the short training time of new subjects by using a transfer learning method based on a fine-tuning strategy. The proposed scheme was tested in four scenarios: intra-session, intra-session long-time use, inter-subject, and inter-subject with a fine-tuning strategy. The proposed attention-based CNN model outperformed the vanilla CNN model and a hybrid CNN-long short-term memory (LSTM) model in intra-session and intra-session long-time use. After fine-tuning with a small amount of data on a new subject, the attention-based CNN model achieved higher decoding accuracy than the vanilla CNN model and lower response time than CNN-LSTM model. Furthermore, the schemes with KF outperformed the schemes without KF in all scenarios. Our proposed scheme improves the decoding accuracy of the traditional CNN model for a single subject by better capturing the temporal information of the surface electromyography signal and increasing the prior knowledge of the system. Additionally, the proposed scheme can be easily transferred to a new subject using only a small amount of data. |
| format | Article |
| id | doaj-art-464b8b48a2574f02b4a5c8dda0496aae |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-464b8b48a2574f02b4a5c8dda0496aae2025-08-20T03:05:39ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102023-01-01311878188710.1109/TNSRE.2023.326226910082987Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter SchemeAnyuan Zhang0https://orcid.org/0000-0001-7854-7071Qi Li1https://orcid.org/0000-0002-2716-449XZhenlan Li2Jiming Li3https://orcid.org/0009-0000-2964-8650School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, ChinaSchool of Computer Science and Technology, Changchun University of Science and Technology, Changchun, ChinaDepartment of Physical Medicine and Rehabilitation, The First Hospital of Jilin University, Changchun, ChinaSchool of Computer Science and Technology, Changchun University of Science and Technology, Changchun, ChinaConvolutional neural network (CNN)-based models are widely used in human movement decoding based on surface electromyography. However, they capture only the spatial information of the surface electromyography and lack prior knowledge of the system, resulting in unsatisfactory decoding accuracy. To address these issues, we propose an attention-based Kalman filter scheme (AKFS), which uses an attention-based CNN model to better extract temporal information and a KF to add prior knowledge of the system. We further solve the problem of insufficient data due to the short training time of new subjects by using a transfer learning method based on a fine-tuning strategy. The proposed scheme was tested in four scenarios: intra-session, intra-session long-time use, inter-subject, and inter-subject with a fine-tuning strategy. The proposed attention-based CNN model outperformed the vanilla CNN model and a hybrid CNN-long short-term memory (LSTM) model in intra-session and intra-session long-time use. After fine-tuning with a small amount of data on a new subject, the attention-based CNN model achieved higher decoding accuracy than the vanilla CNN model and lower response time than CNN-LSTM model. Furthermore, the schemes with KF outperformed the schemes without KF in all scenarios. Our proposed scheme improves the decoding accuracy of the traditional CNN model for a single subject by better capturing the temporal information of the surface electromyography signal and increasing the prior knowledge of the system. Additionally, the proposed scheme can be easily transferred to a new subject using only a small amount of data.https://ieeexplore.ieee.org/document/10082987/Attention mechanismconvolutional neural networkhuman movement decodingKalman filterlong short-term memory |
| spellingShingle | Anyuan Zhang Qi Li Zhenlan Li Jiming Li Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter Scheme IEEE Transactions on Neural Systems and Rehabilitation Engineering Attention mechanism convolutional neural network human movement decoding Kalman filter long short-term memory |
| title | Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter Scheme |
| title_full | Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter Scheme |
| title_fullStr | Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter Scheme |
| title_full_unstemmed | Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter Scheme |
| title_short | Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter Scheme |
| title_sort | upper limb movement decoding scheme based on surface electromyography using attention based kalman filter scheme |
| topic | Attention mechanism convolutional neural network human movement decoding Kalman filter long short-term memory |
| url | https://ieeexplore.ieee.org/document/10082987/ |
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