An Explainable Artificial Intelligence Approach for Force Estimation from Surface-EMG sing the Element Description Method

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

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In recent years, the need for technological advancement has been increasing because of the declining birthrate and aging populations. Motion copying is one such technical development that can effectively reproduce human motion. However, the challenges of conventional motion-copying systems include the lack of operability and loss of touch sensation at the abstraction phase. Then, we focused on the force estimation by surface-EMG for abstracting force information. In this paper, we address this challenge by developing an explainable Artificial Intelligence (XAI) approach that can estimate force using surface-EMG and uses element description method to interpret and identify the components contributing towards that estimation. We evaluated the proposed XAI method across several real-world experiments that confirm its value and contribution towards innovations in motion-copying system.

本文言語English
ホスト出版物のタイトル2023 IEEE 32nd International Symposium on Industrial Electronics, ISIE 2023 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9798350399714
DOI
出版ステータスPublished - 2023
イベント32nd IEEE International Symposium on Industrial Electronics, ISIE 2023 - Helsinki, Finland
継続期間: 2023 6月 192023 6月 21

出版物シリーズ

名前IEEE International Symposium on Industrial Electronics
2023-June

Conference

Conference32nd IEEE International Symposium on Industrial Electronics, ISIE 2023
国/地域Finland
CityHelsinki
Period23/6/1923/6/21

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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