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Techniques of EMG Signal Analysis: Detection, Processing and Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 10903

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Dr. Armando Malanda Trigueros

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Guest Editor

Department of Electrical, Electronics and Communication Engineering, Public University of Navarra, 31006 Pamplona, Spain
Interests: quantitative EMG; neuromuscular jitter; EMG signal classification; biomedical signal processing

Prof. Dr. Daniel Stashuk

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Guest Editor

System Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: quantitative EMG; neuromuscular jitter; EMG signal classification

Dr. Javier Rodriguez-Falces

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Guest Editor

Department of Electrical, Electronics and Communication Engineering, Public University of Navarra, 31006 Pamplona, Spain
Interests: electromyography; M wave; muscle membrane excitability; muscle fatigue; nerve stimulation; conduction velocity

Special Issue Information

Dear Colleagues,

An electromyographic (EMG) signal is a recording of the electrical activity of a skeletal muscle. EMG signals provide muscular morphological, electrophysiological and motor control information. EMG signal analysis plays a major role in clinical diagnosis and treatment of neuromuscular disorders and injuries, in ergonomic assessment of muscular activity, in studies of aging and muscular pain and fatigue, in the development and assessment of rehabilitation therapies and physical exercise strategies, and in control of exoskeletons, artificial limbs and orthotic devices. Due to technological innovations over the last two decades, all of these areas of application have expanded in breadth and depth.

Among these innovations, three deserve special attention. High density surface EMG (HD-SEMG) devices and the associated processing techniques for decomposing recorded signals into trains of motor unit potentials probably represent the most significant development. HD-SEMG offers a deeper and more reliable insight into motor unit physiological properties, motor unit recruitment, and neural coding, allowing and motivating multiple new applications and research works in virtually all areas of EMG signal analysis.

In the last decade, rapid development of new artificial intelligence (AI) algorithms and techniques (deep learning and nature-based algorithms) has allowed major advancements in clinical EMG signal analysis and classification and the procuring of efficient inputs for EMG signal-driven functional prosthesis, exoskeletons and orthotic devices.

Finally, the development of innovative garments and gadgets (wearable devices) that integrate multiple EMG sensors, possibly in combination with mechanical and positional sensors, will facilitate a large number of new applications in sport sciences, ergonomics and rehabilitation.

This issue of Sensors is intended to present the most salient and current developments in algorithmic techniques, sensor devices and applications in the field of EMG signal analysis within the following areas:

Quantitative EMG signal analysis applied to clinical neurophysiology;
HD-SEMG devices, algorithms and applications;
EMG wearable devices;
AI techniques applied to EMG signals;
Use of EMG signals in rehabilitation;
EMG signal-controlled exoskeletons and functional prosthesis.

Dr. Armando Malanda Trigueros
Prof. Dr. Daniel Stashuk
Dr. Javier Rodríguez-Falces
Guest Editors

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Keywords

quantitative EMG
HD-SEMG
wearable devices
artificial intelligence
rehabilitation
exoskeletons
functional prosthesis

Published Papers (3 papers)

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Research

23 pages, 25046 KiB

Open AccessArticle

sEMG and Vibration System Monitoring for Differential Diagnosis in Temporomandibular Joint Disorders

by Małgorzata Kulesa-Mrowiecka, Robert Barański and Maciej Kłaczyński

Sensors 2022, 22(10), 3811; https://doi.org/10.3390/s22103811 - 17 May 2022

Cited by 4 | Viewed by 4200

Abstract

The stomatognathic system represents an important element of human physiology, constituting a part of the digestive, respiratory, and sensory systems. One of the signs of temporomandibular joint disorders (TMD) can be the formation of vibroacoustic and electromyographic (sEMG) phenomena. The aim of the study was to evaluate the effectiveness of temporomandibular joint rehabilitation in patients suffering from locking of the temporomandibular joint (TMJ) articular disc by analysis of vibrations, sEMG registration of masseter muscles, and hypertension of masticatory muscles. In this paper, a new system for the diagnosis of TMD during rehabilitation is proposed, based on the use of vibration and sEMG signals. The operation of the system was illustrated in a case study, a 27-year-old woman with articular dysfunction of the TMJ. The first results of TMD diagnostics using the k-nearest neighbors method are also presented on a group of fifteen people (ten women and five men). Vibroacoustic registration of temporomandibular joints, sEMG registration of masseter muscles, and functional manual analysis of the TMJ were simultaneously assessed before employing splint therapy with stomatognathic physiotherapy. Analysis of vibrations with the monitoring of sEMG in dysfunctions of the TMJ can lead to improve differential diagnosis and can be an objective way of monitoring the rehabilitation process of TMD. Full article

(This article belongs to the Special Issue Techniques of EMG Signal Analysis: Detection, Processing and Applications)

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10 pages, 2816 KiB

Open AccessArticle

Association between the Degree of Pre-Synaptic Dopaminergic Pathway Degeneration and Motor Unit Firing Behavior in Parkinson’s Disease Patients

by Yuichi Nishikawa, Kohei Watanabe, Aleš Holobar, Tetsuya Takahashi, Noriaki Maeda, Hirofumi Maruyama, Shinobu Tanaka and Allison S Hyngstrom

Sensors 2021, 21(19), 6615; https://doi.org/10.3390/s21196615 - 4 Oct 2021

Cited by 2 | Viewed by 2534

Abstract

The relationship between motor unit (MU) firing behavior and the severity of neurodegeneration in Parkinson’s disease (PD) is not clear. This study aimed to elucidate the association between degeneration with dopaminergic pathways and MU firing behavior in people with PD. Fourteen females with PD (age, 72.6 ± 7.2 years, disease duration, 3.5 ± 2.1 years) were enrolled in this study. All participants performed a submaximal, isometric knee extension ramp-up contraction from 0% to 80% of their maximal voluntary contraction strength. We used high-density surface electromyography with 64 electrodes to record the muscle activity of the vastus lateralis muscle and decomposed the signals with the convolution kernel compensation technique to extract the signals of individual MUs. We calculated the degree of degeneration of the central lesion-specific binding ratio by dopamine transporter single-photon emission computed tomography. The primary, novel results were as follows: (1) moderate-to-strong correlations were observed between the degree of degeneration of the central lesion and MU firing behavior; (2) a moderate correlation was observed between clinical measures of disease severity and MU firing behavior; and (3) the methods of predicting central nervous system degeneration from MU firing behavior abnormalities had a high detection accuracy with an area under the curve >0.83. These findings suggest that abnormalities in MU activity can be used to predict central nervous system degeneration following PD. Full article

(This article belongs to the Special Issue Techniques of EMG Signal Analysis: Detection, Processing and Applications)

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10 pages, 3517 KiB

Open AccessArticle

Effect of Acute Static Stretching on the Activation Patterns Using High-Density Surface Electromyography of the Gastrocnemius Muscle during Ramp-Up Task

by Noriaki Maeda, Makoto Komiya, Yuichi Nishikawa, Masanori Morikawa, Shogo Tsutsumi, Tsubasa Tashiro, Kazuki Fukui, Hiroaki Kimura and Yukio Urabe

Sensors 2021, 21(14), 4841; https://doi.org/10.3390/s21144841 - 15 Jul 2021

Cited by 4 | Viewed by 2702

Abstract

This study aimed to evaluate motor unit recruitment during submaximal voluntary ramp contraction in the medial head of the gastrocnemius muscle (MG) by high-density spatial electromyography (SEMG) before and after static stretching (SS) in healthy young adults. SS for gastrocnemius was performed in 15 healthy participants for 2 min. Normalized peak torque by bodyweight of the plantar flexor, muscle activity at peak torque, and muscle activation patterns during ramp-up task were evaluated before and after SS. Motor unit recruitment during the submaximal voluntary contraction of the MG was measured using SEMG when performing submaximal ramp contractions during isometric ankle plantar flexion from 30 to 80% of the maximum voluntary contraction (MVC). To evaluate the changes in the potential distribution of SEMG, the root mean square (RMS), modified entropy, and coefficient of variation (CV) were calculated from the dense surface EMG data when 10% of the MVC force was applied. Muscle activation patterns during the 30 to 80% of MVC submaximal voluntary contraction tasks were significantly changed from 50 to 70% of MVC after SS when compared to before. The variations in motor unit recruitment after SS indicate diverse motor unit recruitments and inhomogeneous muscle activities, which may adversely affect the performance of sports activities. Full article

(This article belongs to the Special Issue Techniques of EMG Signal Analysis: Detection, Processing and Applications)

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