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Open Database for Accurate Upper-Limb Intent Detection Using Electromyography and Reliable Extreme Learning Machines

Programa de Pós-Graduação em Engenharia Elétrica da Universidade Federal do Rio Grande do Sul, Avenue Osvaldo Aranha 103, Porto Alegre 90035-190, Brazil
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Sensors 2019, 19(8), 1864; https://doi.org/10.3390/s19081864
Received: 15 March 2019 / Revised: 12 April 2019 / Accepted: 14 April 2019 / Published: 18 April 2019
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Abstract

Surface Electromyography (sEMG) signal processing has a disruptive technology potential to enable a natural human interface with artificial limbs and assistive devices. However, this biosignal real-time control interface still presents several restrictions such as control limitations due to a lack of reliable signal prediction and standards for signal processing among research groups. Our paper aims to present and validate our sEMG database through the signal classification performed by the reliable forms of our Extreme Learning Machines (ELM) classifiers, used to maintain a more consistent signal classification. To perform the signal processing, we explore the use of a stochastic filter based on the Antonyan Vardan Transform (AVT) in combination with two variations of our Reliable classifiers (denoted R-ELM and R-Regularized ELM (RELM), respectively), to derive a reliability metric from the system, which autonomously selects the most reliable samples for the signal classification. To validate and compare our database and classifiers with related papers, we performed the classification of the whole of Databases 1, 2, and 6 (DB1, DB2, and DB6) of the NINAProdatabase. Our database presented consistent results, while the reliable forms of ELM classifiers matched or outperformed related papers, reaching average accuracies higher than 99 % for the IEEdatabase, while average accuracies of 75.1 %, 79.77 %, and 69.83 % were achieved for NINAPro DB1, DB2, and DB6, respectively. View Full-Text
Keywords: EMG; feedforward neural networks; extreme learning machines; non-iterative classifier; reliability; prosthetic hand EMG; feedforward neural networks; extreme learning machines; non-iterative classifier; reliability; prosthetic hand
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MDPI and ACS Style

Cene, V.H.; Tosin, M.; Machado, J.; Balbinot, A. Open Database for Accurate Upper-Limb Intent Detection Using Electromyography and Reliable Extreme Learning Machines. Sensors 2019, 19, 1864.

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