Abstract
Human-In-The-Loop (HITL) control strategies using surface electromyography (sEMG) face challenges with labeling in supervised learning. Unsupervised regression methods for sEMG signals have limitations in controlling multiple grasp motions. This paper presents two semi-supervised regression approaches using neural networks (NN) for sEMG-based robot hand control. The first approach uses soft-DTW divergence as a loss function for minimally supervised NN training. The second combines Non-Negative Matrix Factorization (NMF) with self-supervised NN regression. Offline tests show the soft-DTW NN performs similarly to a standard MSE-based NN, and the self-supervised regression outperforms traditional unsupervised methods, enabling multiple grasp actions.
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Acknowledgement
This work was partially supported by European Commission’s Horizon Europe Framework Programme with the project IntelliMan under Grant 101070136, by MUR with the project “Sustainable Mobility Center” under Grant CN00000023-CUP J33C22001120001, and by MICS (Made in Italy – Circular and Sustainable) Extended Partnership and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D. 1551.11-10-2022, PE00000004).
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Meattini, R., Bernardini, A., Caporali, A., Palli, G., Melchiorri, C. (2024). Approaches for Exploiting Neural Networks for Semi-supervised Myoelectric Control of Robot Hands. In: Secchi, C., Marconi, L. (eds) European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-031-76424-0_58
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DOI: https://doi.org/10.1007/978-3-031-76424-0_58
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