Affiliation:
1. Department Of Electronics And Communication Engineering, MES College of Engg, Kuttippuram
Abstract
Natural control methods based on surface electromyography (sEMG) and pattern recognition are
promising for hand prosthetics. Several efforts have been carried out to enhance dexterous hand
prosthesis control by impaired individuals. However, the control robustness offered by scientic research is still not sufcient for
many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. This
paper reviews various papers on deep learning approaches to the control of prosthetic hands with EMG signals and made a
comparison on their accuracy.
Reference24 articles.
1. S. Banzi, E. Mainardi, and A. Davalli, “Analisi delle strategie di controllo per protesi di arto superiore in pazienti con amputazioni transomerali o disarticolati di spalla,” ANIPLA—Biosyst., pp. 290–300, 2005.
2. U.K. Prosthetics Services, “Upper Limb Amputations” Amputee Statistical Database for U.K.: 2005/06.
3. M. Atzori, M. Cognolato, and H. Müller, “Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands,” Frontiers in Neurorobotics, vol. 10, 2016.
4. X. Zhai, B. Jelfs, R. H. M. Chan, and C. Tin, “Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network,” Frontiers in Neuroscience, vol. 11, 2017.
5. U. Cote-Allard, C. L. Fall, A. Campeau-Lecours, C. Gosselin, F. Laviolette, and B. Gosselin, “Transfer learning for sEMG hand gestures recognition using convolutional neural networks,” 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017.