| Item type |
SIG Technical Reports(1) |
| 公開日 |
2023-11-14 |
| タイトル |
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タイトル |
Estimation of Contraction Force of Forearm Muscle Using Stretch-Sensor Glove |
| タイトル |
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言語 |
en |
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タイトル |
Estimation of Contraction Force of Forearm Muscle Using Stretch-Sensor Glove |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
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主題 |
ウェアラブル・入力 |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
| 著者所属 |
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Kobe University |
| 著者所属 |
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Kobe University |
| 著者所属 |
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Kobe University |
| 著者所属 |
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Kobe University |
| 著者所属(英) |
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en |
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Kobe University |
| 著者所属(英) |
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en |
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Kobe University |
| 著者所属(英) |
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en |
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Kobe University |
| 著者所属(英) |
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en |
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Kobe University |
| 著者名 |
Adhe, Rahmatullah Sugiharto Suwito P
Ayumi, Ohnishi
Tsutomu, Terada
Masahiko, Tsukaoto
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| 著者名(英) |
Adhe, Rahmatullah Sugiharto Suwito P
Ayumi, Ohnishi
Tsutomu, Terada
Masahiko, Tsukaoto
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| 論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
Estimating a muscle contraction force has become an important method as it provides muscle conditions. Most studies focused on Electromyography (EMG) which requires a self-calibration procedure and its poor noise resistance. Considering the muscle-correlated modality, several studies introduced an excellent correlation of stretch sensors with EMG, thereby eliminating a complicated installation procedure. Therefore, this study proposed a method to estimate the forearm muscle contraction force using the stretch-sensor-based glove. The EMG signals of the forearm muscles and the stretch sensors' resistance values attached to each finger were recorded and trained by support vector regressor (SVR), random forest regressor (RFR), and multi-layer perceptron regressor (MLPR). The root-mean-square value of the EMG linear envelope was extracted as a muscle contraction force parameter and estimated from the stretch sensor values. The estimation performance was confirmed in the condition of grasping six cylinders varying in diameter sizes with natural and maximum grasping force. The result confirmed that the RFR model has the best performance on estimating the contraction force of forearm muscle. |
| 論文抄録(英) |
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内容記述タイプ |
Other |
|
内容記述 |
Estimating a muscle contraction force has become an important method as it provides muscle conditions. Most studies focused on Electromyography (EMG) which requires a self-calibration procedure and its poor noise resistance. Considering the muscle-correlated modality, several studies introduced an excellent correlation of stretch sensors with EMG, thereby eliminating a complicated installation procedure. Therefore, this study proposed a method to estimate the forearm muscle contraction force using the stretch-sensor-based glove. The EMG signals of the forearm muscles and the stretch sensors' resistance values attached to each finger were recorded and trained by support vector regressor (SVR), random forest regressor (RFR), and multi-layer perceptron regressor (MLPR). The root-mean-square value of the EMG linear envelope was extracted as a muscle contraction force parameter and estimated from the stretch sensor values. The estimation performance was confirmed in the condition of grasping six cylinders varying in diameter sizes with natural and maximum grasping force. The result confirmed that the RFR model has the best performance on estimating the contraction force of forearm muscle. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA1221543X |
| 書誌情報 |
研究報告ヒューマンコンピュータインタラクション（HCI）
巻 2023-HCI-205,
号 2,
p. 1-8,
発行日 2023-11-14
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8760 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-HCI23205002.pdf (1.7 MB)
