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A membership-function–based broad learning system for human–robot interaction force estimation under drawing task

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Abstract

Estimating interaction force is of great significance in the field of human-robot interaction (HRI) thanks to its guarantee of interaction safety. To this end, this paper proposes a novel estimation method by leveraging broad learning system (BLS) and human surface electromyography (sEMG) signals. Since the previous sEMG may also contain valuable information of human muscle force, it would cause the estimation to be incomplete and abate the estimation accuracy in the case of neglecting the previous sEMG. To remedy this thorn, a new linear membership function is first developed to calculate contributions of sEMG at different sampling times in the proposed method. Subsequently, the contribution values calculated by the membership function are integrated with features of sEMG to be considered as the input layer of BLS. For extensive studies, five different features extracted from sEMG signals and their combination are explored to estimate the interaction force by the proposed method. Lastly, the performance of the proposed method is compared with those of three well-known methods through experimental test regarding the drawing task. The experimental results confirm that combining the time domain (TD) with frequency domain (FD) features of sEMG can enhance the estimation quality. Moreover, the proposed method outperforms its contenders with respect to estimation accuracy.

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Funding

This work is financially supported by National Nature Science Foundation of China under grant numbers 62203341, 61603284, and 61903286, in part by the Fundamental Research Funds for the Central Universities under grant number 2022IVA044.

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Authors and Affiliations

  1. School of Automation, Wuhan University of Technology, Luoshi Road, Wuhan, 430070, Hubei, China

    Biwei Tang, Ruiqing Li, Jing Luo, Muye Pang & Kui Xiang

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  1. Biwei Tang
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  2. Ruiqing Li
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  3. Jing Luo
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Correspondence to Jing Luo.

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Prior to the experimental study, each participant has been notified and required to provide a written informed consent reviewed and approved by Wuhan University of Technology.

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Tang, B., Li, R., Luo, J. et al. A membership-function–based broad learning system for human–robot interaction force estimation under drawing task. Med Biol Eng Comput 61, 1975–1992 (2023). https://doi.org/10.1007/s11517-023-02821-2

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  • DOI: https://doi.org/10.1007/s11517-023-02821-2

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