Abstract
In this paper, we evaluate the performance of a low-cost surface electromyographic (sEMG) device in the classification of different hand-grip features. To that end, hand-pressure information has been measured together with the sEMG activity of the forearm during the performance of grasping activities with three different finger groups. Results show that affordable sEMG sensoring can accurately differentiate between different grasping poses and is also capable, at the same time, of decoding hand force levels.
This work was supported by University of Alicante through project GRE16-20, Control Platform for a Robotic Hand based on Electromyographic Signals.
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Jover, A., Martí, G., Torres, F., Puente, S.T., Úbeda, A. (2019). Evaluation of Hand-Grip Features Using Low-Cost Electromyography. In: Masia, L., Micera, S., Akay, M., Pons, J. (eds) Converging Clinical and Engineering Research on Neurorehabilitation III. ICNR 2018. Biosystems & Biorobotics, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-030-01845-0_19
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DOI: https://doi.org/10.1007/978-3-030-01845-0_19
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