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Journal of Healthcare Engineering
Volume 2018, Article ID 2350834, 11 pages
https://doi.org/10.1155/2018/2350834
Research Article

sEMG Signal Acquisition Strategy towards Hand FES Control

1División de Investigación en Ingeniería Médica, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Calz. México-Xochimilco No. 289, Col. Arenal de Guadalupe, Tlalpan, 14389 Ciudad de México, Mexico
2Facultad de Ingeniería, Universidad La Salle, Benjamín Franklin 45, Col. Condesa, Cuauhtémoc, 06140 Ciudad de México, Mexico
3LAREMUS, Sección Bioelectrónica, Departamento de Ingeniería Eléctrica, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, 07360 Ciudad de México, Mexico

Correspondence should be addressed to Josefina Gutiérrez-Martínez; moc.liamtoh@zerreitug_anifesoj

Received 11 August 2017; Revised 1 December 2017; Accepted 27 December 2017; Published 14 March 2018

Academic Editor: Kunal Mitra

Copyright © 2018 Cinthya Lourdes Toledo-Peral et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Due to damage of the nervous system, patients experience impediments in their daily life: severe fatigue, tremor or impaired hand dexterity, hemiparesis, or hemiplegia. Surface electromyography (sEMG) signal analysis is used to identify motion; however, standardization of electrode placement and classification of sEMG patterns are major challenges. This paper describes a technique used to acquire sEMG signals for five hand motion patterns from six able-bodied subjects using an array of recording and stimulation electrodes placed on the forearm and its effects over functional electrical stimulation (FES) and volitional sEMG combinations, in order to eventually control a sEMG-driven FES neuroprosthesis for upper limb rehabilitation. A two-part protocol was performed. First, personalized templates to place eight sEMG bipolar channels were designed; with these data, a universal template, called forearm electrode set (FELT), was built. Second, volitional and evoked movements were recorded during FES application. 95% classification accuracy was achieved using two sessions per movement. With the FELT, it was possible to perform FES and sEMG recordings simultaneously. Also, it was possible to extract the volitional and evoked sEMG from the raw signal, which is highly important for closed-loop FES control.