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Myoelectric Control for Upper Limb Prostheses

1
Instituto de Telecomunicaciones y Aplicaciones Multimedia (ITEAM), Departamento de Comunicaciones, Universitat Politècnica de València, 46022 Valencia, Spain
2
Applied Rehabilitation Technology Lab (ART-Lab), Department of Trauma, Orthopedic and Plastic Surgery, University Medical Center Göttingen, 37075 Göttingen, Germany
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1244; https://doi.org/10.3390/electronics8111244
Received: 2 October 2019 / Revised: 24 October 2019 / Accepted: 28 October 2019 / Published: 30 October 2019
(This article belongs to the Section Bioelectronics)
State-of-the-art high-end prostheses are electro-mechanically able to provide a great variety of movements. Nevertheless, in order to functionally replace a human limb, it is essential that each movement is properly controlled. This is the goal of prosthesis control, which has become a growing research field in the last decades, with the ultimate goal of reproducing biological limb control. Therefore, exploration and development of prosthesis control are crucial to improve many aspects of an amputee’s life. Nowadays, a large divergence between academia and industry has become evident in commercial systems. Although several studies propose more natural control systems with promising results, basic one degree of freedom (DoF), a control switching system is the most widely used option in industry because of simplicity, robustness and inertia. A few classification controlled prostheses have emerged in the last years but they are still a low percentage of the used ones. One of the factors that generate this situation is the lack of robustness of more advanced control algorithms in daily life activities outside of laboratory conditions. Because of this, research has shifted towards more functional prosthesis control. This work reviews the most recent literature in upper limb prosthetic control. It covers commonly used variants of possible biological inputs, its processing and translation to actual control, mostly focusing on electromyograms as well as the problems it will have to overcome in near future. View Full-Text
Keywords: myoelectric control; prosthesis; electromyography; EMG; upper limb; feature extraction; data acquisition; sampling frequency; segmentation; machine learning; classification; regression; feedback; human adaptation; co-adaptation; robustness; usability; review myoelectric control; prosthesis; electromyography; EMG; upper limb; feature extraction; data acquisition; sampling frequency; segmentation; machine learning; classification; regression; feedback; human adaptation; co-adaptation; robustness; usability; review
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MDPI and ACS Style

Igual, C.; Pardo, L.A., Jr.; Hahne, J.M.; Igual, J. Myoelectric Control for Upper Limb Prostheses. Electronics 2019, 8, 1244.

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