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An EMG-Based GRU Model for Estimating Foot Pressure to Support Active Ankle Orthosis Development
http://hdl.handle.net/10458/0002001690
http://hdl.handle.net/10458/0002001690c69059e3-696f-483d-86b0-a30997f8ec72
| 名前 / ファイル | ライセンス | アクション |
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| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||||
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| 公開日 | 2025-08-27 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | An EMG-Based GRU Model for Estimating Foot Pressure to Support Active Ankle Orthosis Development | |||||||||||||
| 言語 | en | |||||||||||||
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| 言語 | eng | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| キーワード | AAFO | |||||||||||||
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| 言語 | en | |||||||||||||
| キーワード | foot pressure estimation | |||||||||||||
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| 言語 | en | |||||||||||||
| キーワード | gait analysis | |||||||||||||
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| 言語 | en | |||||||||||||
| キーワード | GRU neural network | |||||||||||||
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| 言語 | en | |||||||||||||
| キーワード | surface EMG | |||||||||||||
| 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 |
Gunaratne, Praveen Nuwantha
× Gunaratne, Praveen Nuwantha
× 田村, 宏樹
WEKO
7150
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| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | As populations age, particularly in countries like Japan, mobility impairments related to ankle joint dysfunction, such as foot drop, instability, and reduced gait adaptability, have become a significant concern. Active ankle–foot orthoses (AAFO) offer targeted support during walking; however, most existing systems rely on rule-based or threshold-based control, which are often limited to sagittal plane movements and lacking adaptability to subject-specific gait variations. This study proposes an approach driven by neuromuscular activation using surface electromyography (EMG) and a Gated Recurrent Unit (GRU)-based deep learning model to predict plantar pressure distributions at the heel, midfoot, and toe regions during gait. EMG signals were collected from four key ankle muscles, and plantar pressures were recorded using a customized sandal-integrated force-sensitive resistor (FSR) system. The data underwent comprehensive preprocessing and segmentation using a sliding window method. Root mean square (RMS) values were extracted as the primary input feature due to their consistent performance in capturing muscle activation intensity. The GRU model successfully generalized across subjects, enabling the accurate real-time inference of critical gait events such as heel strike, mid-stance, and toe off. This biomechanical evaluation demonstrated strong signal compatibility, while also identifying individual variations in electromechanical delay (EMD). The proposed predictive framework offers a scalable and interpretable approach to improving real-time AAFO control by synchronizing assistance with user-specific gait dynamics. | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : Sensors 巻 25, 号 11, p. 3558, 発行日 2025-06-05 |
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| 出版者 | MDPI | |||||||||||||
| 言語 | en | |||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 14248220 | |||||||||||||
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| 関連タイプ | isVersionOf | |||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.3390/s25113558 | |||||||||||||
| 権利 | ||||||||||||||
| 権利情報 | © 2025 by the authors. | |||||||||||||
| 言語 | en | |||||||||||||
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| 出版タイプ | VoR | |||||||||||||
