Surface EMG multichannel array using active dry sensors for forearm signal extraction

Reategui, Julio; Callupe, Rocío

doi:10.1109/intercon.2017.8079699

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…The electrode arrays found in the literature have a geometrical matrix design, and the signals acquired can be treated as onedimensional vectors, 2D images, or 3D heat maps (Barbero et al, 2012b;Dai et al, 2019;Guzmán et al, 2011;Moin et al, 2018;Peng et al, 2016;Reategui and Callupe, 2017;Rojas-Martínez et al, 2013;Rui Ma et al, 2010;Ruvalcaba et al, 2017;Topalović et al, 2019). These types of arrays are advantageous when one desires to acquire a larger number of channels, and they are mainly used to evaluate areas of muscle contribution during upper limb isometric contractions.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Dry Electrode Array for sEMG Recording and Hand Movement Recognition

Toledo-Peral,

Martín-Vignon-Whaley,

Mercado-Gutierrez

et al. 2024

Ing. Inv.

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Surface electromyography (sEMG) signals are an indirect measurement of muscle activity, and their applications range from biomechanics to control and rehabilitation. Hand movement recognition is a very difficult endeavor due to forearm anatomy. Hence, a multichannel approach for signal acquisition and processing is required. Conventional electrodes can limit the ease-of-use and repeatability of multi-channel sEMG recordings. New techniques have been proposed in this regard, with dry electrodes being one of them. Dry electrode technology has enabled the design of better donning and doffing procedures for multichannel sEMG recording, particularly for rehabilitation and prosthetic applications. However, there is a debate about the quality of the signals recorded with them and their usefulness for the recognition of multiple hand movements. To mitigate these quality issues, this work proposes an array of reusable stainless steel dry electrodes for multichannel sEMG recording with a design that facilitates its positioning on the forearm. The dry electrodes were characterized through electrical impedance measures and a Bland-Altman test. They were found to have similar characteristics to standard, disposable sEMG pre-gelled electrodes. For placement repeatability and application feasibility, an anatomy-based electrode positioning protocol was implemented with 17 healthy subjects and six hand movements. To evaluate the application feasibility of the electrode array, a feed-forward artificial neural network was trained to classify signals from the six movements, with a 97,86±0,58% accuracy. The amplitude of the sEMG signals for two antagonist movements was compared, finding a 24,81% variation. The dry electrode array showed feasibility in acquiring and classifying sEMG signals of hand movements with high accuracy.

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Section: Introductionmentioning

confidence: 99%

Development and Validation of a Dry Electrode Array for sEMG Recording and Hand Movement Recognition

Toledo-Peral,

Martín-Vignon-Whaley,

Mercado-Gutierrez

et al. 2024

Ing. Inv.

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“…sEMG signals have been widely used as a control signal for rehabilitation systems and applications for a long time now [31]. This approach has several advantages regarding signal acquisition which allows the user to move freely depending on the type of hardware used, including wearable arrays, wireless systems, and even implantable electrodes [32][33][34]. Additionally, there is a variety of electrodes and electrode types, shapes, and arrays that can suit different applications and needs [35,36].…”

Section: Introductionmentioning

confidence: 99%

Virtual/Augmented Reality for Rehabilitation Applications Using Electromyography as Control/Biofeedback: Systematic Literature Review

et al. 2022

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Virtual reality (VR) and augmented reality (AR) are engaging interfaces that can be of benefit for rehabilitation therapy. However, they are still not widely used, and the use of surface electromyography (sEMG) signals is not established for them. Our goal is to explore whether there is a standardized protocol towards therapeutic applications since there are not many methodological reviews that focus on sEMG control/feedback. A systematic literature review using the PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology is conducted. A Boolean search in databases was performed applying inclusion/exclusion criteria; articles older than 5 years and repeated were excluded. A total of 393 articles were selected for screening, of which 66.15% were excluded, 131 records were eligible, 69.46% use neither VR/AR interfaces nor sEMG control; 40 articles remained. Categories are, application: neurological motor rehabilitation (70%), prosthesis training (30%); processing algorithm: artificial intelligence (40%), direct control (20%); hardware: Myo Armband (22.5%), Delsys (10%), proprietary (17.5%); VR/AR interface: training scene model (25%), videogame (47.5%), first-person (20%). Finally, applications are focused on motor neurorehabilitation after stroke/amputation; however, there is no consensus regarding signal processing or classification criteria. Future work should deal with proposing guidelines to standardize these technologies for their adoption in clinical practice.

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The Reusable Electrode of EMG Sensor for Capturing The Calf Muscle Activities

Analia

Sulistyo

Iglesias

et al. 2020

2020 3rd International Conference on Applied Engineering (ICAE)

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Surface EMG multichannel array using active dry sensors for forearm signal extraction

Cited by 3 publications

References 2 publications

Development and Validation of a Dry Electrode Array for sEMG Recording and Hand Movement Recognition

Development and Validation of a Dry Electrode Array for sEMG Recording and Hand Movement Recognition

Virtual/Augmented Reality for Rehabilitation Applications Using Electromyography as Control/Biofeedback: Systematic Literature Review

The Reusable Electrode of EMG Sensor for Capturing The Calf Muscle Activities

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