Abstract
In this paper we propose a novel synergy-based myocontrol scheme for finger force estimation and classification which is able to simultaneously control 4 fingers with a training phase based only on individual-finger data. The proposed method has been tested using the online-available NinaPro database and validated in a preliminary experiment conducted with the use of a hand-exoskeleton. Results show how the presented approach outperforms considerably the linear regression method which is considered standard approach in myoelectric control. The low error rate obtained (smaller than 10% of the targeted force) and the effectiveness in decreasing the number of false activation open the possibilities for future uses in fields such as haptics and neuro-rehabilitation.
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Acknowledgments
This work has been partially funded by: the EU Horizon2020 project nr. 644839 ICT-23-2014 CENTAURO; the national PRIN-2015 ModuLimb (Prot. 2015HFWRYY), the RONDA project (Regione Toscana, Italy FAS Salute 2014 program).
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Murciego, L.P., Barsotti, M., Frisoli, A. (2018). Synergy-Based Multi-fingers Forces Reconstruction and Discrimination from Forearm EMG. In: Prattichizzo, D., Shinoda, H., Tan, H., Ruffaldi, E., Frisoli, A. (eds) Haptics: Science, Technology, and Applications. EuroHaptics 2018. Lecture Notes in Computer Science(), vol 10894. Springer, Cham. https://doi.org/10.1007/978-3-319-93399-3_19
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