Articles | Volume 12, issue 1
https://doi.org/10.5194/ms-12-69-2021
https://doi.org/10.5194/ms-12-69-2021
Research article
 | 
04 Feb 2021
Research article |  | 04 Feb 2021

Design and multichannel electromyography system-based neural network control of a low-cost myoelectric prosthesis hand

Saygin Siddiq Ahmed, Ahmed R. J. Almusawi, Bülent Yilmaz, and Nuran Dogru

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Subject: Dynamics and Control | Techniques and Approaches: Experiment and Best Practice
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Cited articles

Ahmed, S. S.: EMG SIGNAL 1, available at: https://youtu.be/zBjL8MGIWzk (last access: 6 July 2020), 2019a. 
Ahmed, S. S.: PROSTHETIC HAND, available at: https://youtu.be/BBv-TYpBaR4, last access: 30 December 2019b. 
Ahmed, S. S.: 3D PRINTER, available at: https://youtu.be/Xei3XgUa_-4, (last access: 17 December 2020), 2019c. 
Balbinot, A., Júnior, A. S., and Favieiro, G. W.: Decoding arm movements by myoelectric signal and artificial neural networks, Intelligent Control and Automation, 4, 87–93, https://doi.org/10.4236/ica.2013.41012, 2013. 
Bennett, D. A., Dalley, S. A., Truex, D., and Goldfarb, M.: A multigrasp hand prosthesis for providing precision and conformal grasps, IEEE-ASME T. Mech., 20, 1697–1704, https://doi.org/10.1109/TMECH.2014.2349855 2015. 
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Short summary
A program is designed for the myoelectric hand with two control pathways, one for the thumb (controlled utilizing medial nerves) and the second for the rest of the fingers (controlled by the side nerves). The robotic hand was first printed with the use of a 3D printer.