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

doi:10.1109/ISIE51358.2023.10228049

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

Department of System Design Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	2023 IEEE 32nd International Symposium on Industrial Electronics, ISIE 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350399714
DOIs	https://doi.org/10.1109/ISIE51358.2023.10228049
Publication status	Published - 2023
Event	32nd IEEE International Symposium on Industrial Electronics, ISIE 2023 - Helsinki, Finland Duration: 2023 Jun 19 → 2023 Jun 21

Publication series

Name	IEEE International Symposium on Industrial Electronics
Volume	2023-June

Conference

Conference	32nd IEEE International Symposium on Industrial Electronics, ISIE 2023
Country/Territory	Finland
City	Helsinki
Period	23/6/19 → 23/6/21

Keywords

Bilateral AI
Element Description Method
Explainable AI
Force Estimation
Interpretable Motion Copying
Motion-Copying System
Surface-Electromyography

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering

Access to Document

10.1109/ISIE51358.2023.10228049

Cite this

Sodenaga, D., Takeuchi, I., Silva, D. D., & Katsura, S. (2023). An Explainable Artificial Intelligence Approach for Force Estimation from Surface-EMG sing the Element Description Method. In 2023 IEEE 32nd International Symposium on Industrial Electronics, ISIE 2023 - Proceedings (IEEE International Symposium on Industrial Electronics; Vol. 2023-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIE51358.2023.10228049

An Explainable Artificial Intelligence Approach for Force Estimation from Surface-EMG sing the Element Description Method. / Sodenaga, Daiki; Takeuchi, Issei; Silva, Daswin De et al.
2023 IEEE 32nd International Symposium on Industrial Electronics, ISIE 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (IEEE International Symposium on Industrial Electronics; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sodenaga, D, Takeuchi, I, Silva, DD & Katsura, S 2023, An Explainable Artificial Intelligence Approach for Force Estimation from Surface-EMG sing the Element Description Method. in 2023 IEEE 32nd International Symposium on Industrial Electronics, ISIE 2023 - Proceedings. IEEE International Symposium on Industrial Electronics, vol. 2023-June, Institute of Electrical and Electronics Engineers Inc., 32nd IEEE International Symposium on Industrial Electronics, ISIE 2023, Helsinki, Finland, 23/6/19. https://doi.org/10.1109/ISIE51358.2023.10228049

Sodenaga D, Takeuchi I, Silva DD, Katsura S. An Explainable Artificial Intelligence Approach for Force Estimation from Surface-EMG sing the Element Description Method. In 2023 IEEE 32nd International Symposium on Industrial Electronics, ISIE 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (IEEE International Symposium on Industrial Electronics). doi: 10.1109/ISIE51358.2023.10228049

Sodenaga, Daiki ; Takeuchi, Issei ; Silva, Daswin De et al. / An Explainable Artificial Intelligence Approach for Force Estimation from Surface-EMG sing the Element Description Method. 2023 IEEE 32nd International Symposium on Industrial Electronics, ISIE 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (IEEE International Symposium on Industrial Electronics).

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

abstract = "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.",

keywords = "Bilateral AI, Element Description Method, Explainable AI, Force Estimation, Interpretable Motion Copying, Motion-Copying System, Surface-Electromyography",

author = "Daiki Sodenaga and Issei Takeuchi and Silva, {Daswin De} and Seiichiro Katsura",

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AB - 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.

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

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