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Movement recognition via channel-activation-wise sEMG attention
http://hdl.handle.net/10061/0002000612
http://hdl.handle.net/10061/000200061263f3feee-1d55-4b0f-9d4b-3a2803aa62b2
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||
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| 公開日 | 2024-10-18 | |||||||||
| タイトル | ||||||||||
| タイトル | Movement recognition via channel-activation-wise sEMG attention | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | sEMG | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Movement recognition | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Gestures classification | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | Transformer | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ | journal article | |||||||||
| アクセス権 | ||||||||||
| アクセス権 | open access | |||||||||
| 著者 |
Zhang, Jiaxuan
× Zhang, Jiaxuan
× 松田, 裕貴× Fujimoto, Manato
× 諏訪, 博彦× 安本, 慶一 |
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Context: Surface electromyography (sEMG) signals contain rich information recorded from muscle movements and therefore reflect the user's intention. sEMG has seen dominant applications in rehabilitation, clinical diagnosis as well as human engineering, etc. However, current feature extraction methods for sEMG signals have been seriously limited by their stochasticity, transiency, and non-stationarity. Objective: Our objective is to combat the difficulties induced by the aforementioned downsides of sEMG and thereby extract representative features for various downstream movement recognition. Method: We propose a novel 3-axis view of sEMG features composed of temporal, spatial, and channel-wise summary. We leverage the state-of-the-art architecture Transformer to enforce efficient parallel search and to get rid of limitations imposed by previous work in gesture classification. The transformer model is designed on top of an attention-based module, which allows for the extraction of global contextual relevance among channels and the use of this relevance for sEMG recognition. Results: We compared the proposed method against existing methods on two Ninapro datasets consisting of data from both healthy people and amputees. Experimental results show the proposed method attains the state-of-the-art (SOTA) accuracy on both datasets. We further show that the proposed method enjoys strong generalization ability: a new SOTA is achieved by pretraining the model on a different dataset followed by fine-tuning it on the target dataset. |
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| 書誌情報 |
en : Methods 巻 218, p. 39-47, 発行日 2023-07-20 |
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| 出版者 | ||||||||||
| 出版者 | Elsevier | |||||||||
| ISSN | ||||||||||
| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 1046-2023 | |||||||||
| 出版者版DOI | ||||||||||
| 関連タイプ | isReplacedBy | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1016/j.ymeth.2023.06.011 | |||||||||
| 出版者版URI | ||||||||||
| 関連タイプ | isReplacedBy | |||||||||
| 識別子タイプ | URI | |||||||||
| 関連識別子 | https://www.sciencedirect.com/science/article/pii/S1046202323001093 | |||||||||
| 権利 | ||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by/4.0/ | |||||||||
| 権利情報 | $00A9 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | NA | |||||||||
