Prediction of Fingertip Force Based on the Muscle Characteristics Using Element Description Method

Daiki Sodenaga, Issei Takeuchi, Daswin De Silva, Seiichiro Katsura

研究成果: Conference contribution

抄録

Human motion prediction based on the biological signal has been improved because of the improvement of an artificial intelligent (AI) technology. Conventional methods to predict a human motion have been based on a machine learning such as a neural network, a regression model and so on. From the above, the model by them cannot generate the model whose calculation process is not clear. Then, it is impossible to interpret the relationship between input and output information. In this paper, the element description method (EDM) was applied to generate the model and the prediction of human motion and the analysis about muscle characteristics had been done. An EDM is one of the system identification methods and it is possible to interpret the relationship between input and output because it can generate a block diagram of the model. Especially, the fingertip force and the surface-electromyography (sEMG) of the muscles which move the joint were focused in this paper Moreover, the fingertip force was estimated from the SEMG and it was also done to analyze the muscle characteristics based on the model by an EDM.

本文言語English
ホスト出版物のタイトル2024 33rd International Symposium on Industrial Electronics, ISIE 2024 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9798350394085
DOI
出版ステータスPublished - 2024
イベント33rd International Symposium on Industrial Electronics, ISIE 2024 - Ulsan, Korea, Republic of
継続期間: 2024 6月 182024 6月 21

出版物シリーズ

名前IEEE International Symposium on Industrial Electronics
ISSN(印刷版)2163-5137
ISSN(電子版)2163-5145

Conference

Conference33rd International Symposium on Industrial Electronics, ISIE 2024
国/地域Korea, Republic of
CityUlsan
Period24/6/1824/6/21

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 制御およびシステム工学

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