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Article

A Way of Bionic Control Based on EI, EMG, and FMG Signals

1
Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia
2
Medical Information Technology, RWTH Aachen University, 52074 Aachen, Germany
3
Kotelnikov Institute of Radioengineering and Electronics (IRE) of Russian Academy of Sciences, 125009 Moscow, Russia
*
Author to whom correspondence should be addressed.
Academic Editor: Michael E. Hahn
Sensors 2022, 22(1), 152; https://doi.org/10.3390/s22010152
Received: 9 November 2021 / Revised: 7 December 2021 / Accepted: 22 December 2021 / Published: 27 December 2021
(This article belongs to the Special Issue Bioimpedance Sensors: Instrumentation, Models, and Applications)
Creating highly functional prosthetic, orthotic, and rehabilitation devices is a socially relevant scientific and engineering task. Currently, certain constraints hamper the development of such devices. The primary constraint is the lack of an intuitive and reliable control interface working between the organism and the actuator. The critical point in developing these devices and systems is determining the type and parameters of movements based on control signals recorded on an extremity. In the study, we investigate the simultaneous acquisition of electric impedance (EI), electromyography (EMG), and force myography (FMG) signals during basic wrist movements: grasping, flexion/extension, and rotation. For investigation, a laboratory instrumentation and software test setup were made for registering signals and collecting data. The analysis of the acquired signals revealed that the EI signals in conjunction with the analysis of EMG and FMG signals could potentially be highly informative in anthropomorphic control systems. The study results confirm that the comprehensive real-time analysis of EI, EMG, and FMG signals potentially allows implementing the method of anthropomorphic and proportional control with an acceptable delay. View Full-Text
Keywords: electrical impedance; electromyogram; force myogram; sensor system; simultaneous acquisition; neuromuscular interface; prosthesis; orthosis electrical impedance; electromyogram; force myogram; sensor system; simultaneous acquisition; neuromuscular interface; prosthesis; orthosis
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MDPI and ACS Style

Briko, A.; Kapravchuk, V.; Kobelev, A.; Hammoud, A.; Leonhardt, S.; Ngo, C.; Gulyaev, Y.; Shchukin, S. A Way of Bionic Control Based on EI, EMG, and FMG Signals. Sensors 2022, 22, 152. https://doi.org/10.3390/s22010152

AMA Style

Briko A, Kapravchuk V, Kobelev A, Hammoud A, Leonhardt S, Ngo C, Gulyaev Y, Shchukin S. A Way of Bionic Control Based on EI, EMG, and FMG Signals. Sensors. 2022; 22(1):152. https://doi.org/10.3390/s22010152

Chicago/Turabian Style

Briko, Andrey, Vladislava Kapravchuk, Alexander Kobelev, Ahmad Hammoud, Steffen Leonhardt, Chuong Ngo, Yury Gulyaev, and Sergey Shchukin. 2022. "A Way of Bionic Control Based on EI, EMG, and FMG Signals" Sensors 22, no. 1: 152. https://doi.org/10.3390/s22010152

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