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EMG Sensors and Signal Processing Technologies

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 21683

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Special Issue Editors

Prof. Dr. Evelyn Morin

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Guest Editor

Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada
Interests: high-density EMG; EMG-based force and motion estimation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Usha Kuruganti

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Guest Editor

Faculty of Kinesiology and Co-Director of the Andrew and Marjorie McCain Human Performance Laboratory, University of New Brunswick, Fredericton, NB, Canada
Interests: high-density EMG; neuromuscular physiology; prosthetics; human factors engineering

Prof. Dr. Adrian Chan

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Guest Editor

Department of Systems & Computer Engineering and Director, Research and Education in Accessibility, Design and Innovation (READi), Carleton University, Ottawa, ON, Canada
Interests: non-invasive sensor systems; biomedical signal processing and image processing; accessibility

Special Issue Information

Dear Colleagues,

There has recently been rapid technological progress in EMG sensor and measurement technology, as well as in the development of advanced signal processing methods. Most notably, the development and deployment of high-density EMG sensors and recording systems, and the application of deep learning and artificial intelligence (AI) methods have underpinned advances in EMG-based force prediction, understanding neural control of movement, neurorehabilitation research, and the use of EMG in clinical applications.

We invite original research and review article on the use of advanced EMG sensing technologies and processing techniques to expand our understanding of muscle function and for clinical and nonclinical applications for submission to this Special Issue.

Potential topics include but are not limited to:

EMG-based force and movement estimation
EMG in myoelectric control
EMG in gait analysis
EMG quality analysis
Muscle synergy analysis and applications
Pelvic floor muscle EMG and applications
EMG in neurorehabilitation
EMG in biomechanics
EMG in ergonomics

Prof. Dr. Evelyn Morin
Prof. Dr. Usha Kuruganti
Prof. Dr. Adrian Chan
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

high-density EMG
EMG-based force modelling
myoelectric control
muscle synergy
gait analysis
EMG quality analysis
neural control

Published Papers (10 papers)

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Research

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15 pages, 18707 KiB

Open AccessArticle

Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes

by Hope O. Shaw, Kirstie M. Devin, Jinghua Tang and Liudi Jiang

Sensors 2024, 24(8), 2383; https://doi.org/10.3390/s24082383 - 9 Apr 2024

Viewed by 452

Abstract

Classification-based myoelectric control has attracted significant interest in recent years, leading to prosthetic hands with advanced functionality, such as multi-grip hands. Thus far, high classification accuracies have been achieved by increasing the number of surface electromyography (sEMG) electrodes or adding other sensing mechanisms. While many prescribed myoelectric hands still adopt two-electrode sEMG systems, detailed studies on signal processing and classification performance are still lacking. In this study, nine able-bodied participants were recruited to perform six typical hand actions, from which sEMG signals from two electrodes were acquired using a Delsys Trigno Research+ acquisition system. Signal processing and machine learning algorithms, specifically, linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines (SVM), were used to study classification accuracies. Overall classification accuracy of 93 ± 2%, action-specific accuracy of 97 ± 2%, and F1-score of 87 ± 7% were achieved, which are comparable with those reported from multi-electrode systems. The highest accuracies were achieved using SVM algorithm compared to LDA and KNN algorithms. A logarithmic relationship between classification accuracy and number of features was revealed, which plateaued at five features. These comprehensive findings may potentially contribute to signal processing and machine learning strategies for commonly prescribed myoelectric hand systems with two sEMG electrodes to further improve functionality. Full article

(This article belongs to the Special Issue EMG Sensors and Signal Processing Technologies)

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13 pages, 9646 KiB

Open AccessArticle

Design, Fabrication and Evaluation of a Stretchable High-Density Electromyography Array

by Rejin John Varghese, Matteo Pizzi, Aritra Kundu, Agnese Grison, Etienne Burdet and Dario Farina

Sensors 2024, 24(6), 1810; https://doi.org/10.3390/s24061810 - 11 Mar 2024

Viewed by 1103

Abstract

The adoption of high-density electrode systems for human–machine interfaces in real-life applications has been impeded by practical and technical challenges, including noise interference, motion artefacts and the lack of compact electrode interfaces. To overcome some of these challenges, we introduce a wearable and stretchable electromyography (EMG) array, and present its design, fabrication methodology, characterisation, and comprehensive evaluation. Our proposed solution comprises dry-electrodes on flexible printed circuit board (PCB) substrates, eliminating the need for time-consuming skin preparation. The proposed fabrication method allows the manufacturing of stretchable sleeves, with consistent and standardised coverage across subjects. We thoroughly tested our developed prototype, evaluating its potential for application in both research and real-world environments. The results of our study showed that the developed stretchable array matches or outperforms traditional EMG grids and holds promise in furthering the real-world translation of high-density EMG for human–machine interfaces. Full article

(This article belongs to the Special Issue EMG Sensors and Signal Processing Technologies)

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15 pages, 11911 KiB

Open AccessArticle

Changes in Electromyographic Activity of the Dominant Arm Muscles during Forehand Stroke Phases in Wheelchair Tennis

by Khaled Abuwarda and Abdel-Rahman Akl

Sensors 2023, 23(20), 8623; https://doi.org/10.3390/s23208623 - 21 Oct 2023

Cited by 1 | Viewed by 1505

Abstract

The aim of this study was to determine the muscle activations of the dominant arm during the forehand stroke of wheelchair tennis. Five players participated in the present study (age: 32.6 ± 9.9 years; body mass: 63.8 ± 3.12 kg; height: 164.4 ± 1.7 cm). The electrical muscle activity of six dominant arm muscles was recorded using an sEMG system. A significant effect of the muscle’s activity was observed, and it was shown that the muscle activation was significantly higher in the execution phase compared to the preparation phase in the anterior deltoid and biceps brachii (34.98 ± 10.23% and 29.13 ± 8.27%, p < 0.001); the posterior deltoid, triceps brachii, flexor carpi radialis, and extensor carpi radialis were higher in the follow-through phase than in the execution phase (16.43 ± 11.72%, 16.96 ± 12.19%, 36.23 ± 21.47% and 19.13 ± 12.55%, p < 0.01). In conclusion, it was determined that the muscle activations of the dominant arm muscles demonstrate variances throughout the phases of the forehand stroke. Furthermore, the application of electromyographic analysis to the primary arm muscles has been beneficial in understanding the muscular activity of the shoulder, elbow, and wrist throughout the various phases of the forehand stroke in wheelchair tennis. Full article

(This article belongs to the Special Issue EMG Sensors and Signal Processing Technologies)

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33 pages, 10593 KiB

Open AccessArticle

Wavelet Analysis of Respiratory Muscle sEMG Signals during the Physiological Breakpoint of Static Dry End-Expiratory Breath-Holding in Naive Apneists: A Pilot Study

by Nataša Ž. Mišić, Mirko Ostojić, Saša Cvetković, Petar Miodragović, Zdravko Aničić, Anita Kovačić Popović and Đorđe Stefanović

Sensors 2023, 23(16), 7200; https://doi.org/10.3390/s23167200 - 16 Aug 2023

Cited by 1 | Viewed by 1061

Abstract

The wavelet spectral characteristics of three respiratory muscle signals (scalenus (SC), parasternal intercostal (IC), and rectus abdominis (RA)) and one locomotor muscle (brachioradialis (BR)) were analyzed in the time–frequency (T-F) domain during voluntary breath-holding (BH), with a focus on the physiological breakpoint that is commonly considered the first involuntary breathing movement (IBM) that signals the end of the easy-going phase of BH. The study was performed for an end-expiratory BH physiological breaking point maneuver on twelve healthy, physically active, naive breath-holders/apneists (six professional athletes; six recreational athletes, and two individuals in the post-COVID-19 period) using surface electromyography (sEMG). We observed individual effects that were dependent on muscle oxygenation and each person’s fitness, which were consistent with the mechanism of motor unit (MU) recruitment and the transition of slow-twitch oxidative (type 1) to fast-twitch glycolytic (type 2) muscle fibers. Professional athletes had longer BH durations (BHDs) and strong hypercapnic responses regarding the expiratory RA muscle, which is activated abruptly at higher BHDs in a person-specific range below 250 Hz and is dependent on the BHD. This is in contrast with recreational athletes, who had strong hypoxic responses regarding inspiratory IC muscle, which is activated faster and gradually in the frequency range of 250–450 Hz (independent of the person and BHD). This pilot study preliminarily indicates that it is possible to noninvasively assess the physiological characteristics of skeletal muscles, especially oxygenation, and improve physical fitness tests by determining the T-F features of elevated myoelectric IC and RA activity during BH. Full article

(This article belongs to the Special Issue EMG Sensors and Signal Processing Technologies)

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18 pages, 6317 KiB

Open AccessArticle

IRDC-Net: An Inception Network with a Residual Module and Dilated Convolution for Sign Language Recognition Based on Surface Electromyography

by Xiangrui Wang, Lu Tang, Qibin Zheng, Xilin Yang and Zhiyuan Lu

Sensors 2023, 23(13), 5775; https://doi.org/10.3390/s23135775 - 21 Jun 2023

Cited by 2 | Viewed by 1305

Abstract

Deaf and hearing-impaired people always face communication barriers. Non-invasive surface electromyography (sEMG) sensor-based sign language recognition (SLR) technology can help them to better integrate into social life. Since the traditional tandem convolutional neural network (CNN) structure used in most CNN-based studies inadequately captures the features of the input data, we propose a novel inception architecture with a residual module and dilated convolution (IRDC-net) to enlarge the receptive fields and enrich the feature maps, applying it to SLR tasks for the first time. This work first transformed the time domain signal into a time–frequency domain using discrete Fourier transformation. Second, an IRDC-net was constructed to recognize ten Chinese sign language signs. Third, the tandem CNN networks VGG-net and ResNet-18 were compared with our proposed parallel structure network, IRDC-net. Finally, the public dataset Ninapro DB1 was utilized to verify the generalization performance of the IRDC-net. The results showed that after transforming the time domain sEMG signal into the time–frequency domain, the classification accuracy (acc) increased from 84.29% to 91.70% when using the IRDC-net on our sign language dataset. Furthermore, for the time–frequency information of the public dataset Ninapro DB1, the classification accuracy reached 89.82%; this value is higher than that achieved in other recent studies. As such, our findings contribute to research into SLR tasks and to improving deaf and hearing-impaired people’s daily lives. Full article

(This article belongs to the Special Issue EMG Sensors and Signal Processing Technologies)

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14 pages, 1960 KiB

Open AccessArticle

Surface Electromyography-Driven Parameters for Representing Muscle Mass and Strength

by Joo Hye Sung, Seol-Hee Baek, Jin-Woo Park, Jeong Hwa Rho and Byung-Jo Kim

Sensors 2023, 23(12), 5490; https://doi.org/10.3390/s23125490 - 10 Jun 2023

Cited by 1 | Viewed by 1701

Abstract

The need for developing a simple and effective assessment tool for muscle mass has been increasing in a rapidly aging society. This study aimed to evaluate the feasibility of the surface electromyography (sEMG) parameters for estimating muscle mass. Overall, 212 healthy volunteers participated in this study. Maximal voluntary contraction (MVC) strength and root mean square (RMS) values of motor unit potentials from surface electrodes on each muscle (biceps brachii, triceps brachii, biceps femoris, rectus femoris) during isometric exercises of elbow flexion (EF), elbow extension (EE), knee flexion (KF), knee extension (KE) were acquired. New variables (MeanRMS, MaxRMS, and RatioRMS) were calculated from RMS values according to each exercise. Bioimpedance analysis (BIA) was performed to determine the segmental lean mass (SLM), segmental fat mass (SFM), and appendicular skeletal muscle mass (ASM). Muscle thicknesses were measured using ultrasonography (US). sEMG parameters showed positive correlations with MVC strength, SLM, ASM, and muscle thickness measured by US, but showed negative correlations with SFM. An equation was developed for ASM: ASM = −26.04 + 20.345 × Height + 0.178 × weight − 2.065 × (1, if female; 0, if male) + 0.327 × RatioRMS(KF) + 0.965 × MeanRMS(EE) (SEE = 1.167, adjusted R² = 0.934). sEMG parameters in controlled conditions may represent overall muscle strength and muscle mass in healthy individuals. Full article

(This article belongs to the Special Issue EMG Sensors and Signal Processing Technologies)

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16 pages, 1365 KiB

Open AccessArticle

Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements

by Emma Farago and Adrian D. C. Chan

Sensors 2023, 23(10), 4759; https://doi.org/10.3390/s23104759 - 15 May 2023

Cited by 1 | Viewed by 1247

Abstract

High-density electromyography (HD-EMG) arrays allow for the study of muscle activity in both time and space by recording electrical potentials produced by muscle contractions. HD-EMG array measurements are susceptible to noise and artifacts and frequently contain some poor-quality channels. This paper proposes an interpolation-based method for the detection and reconstruction of poor-quality channels in HD-EMG arrays. The proposed detection method identified artificially contaminated channels of HD-EMG for signal-to-noise ratio (SNR) levels 0 dB and lower with ≥99.9% precision and ≥97.6% recall. The interpolation-based detection method had the best overall performance compared with two other rule-based methods that used the root mean square (RMS) and normalized mutual information (NMI) to detect poor-quality channels in HD-EMG data. Unlike other detection methods, the interpolation-based method evaluated channel quality in a localized context in the HD-EMG array. For a single poor-quality channel with an SNR of 0 dB, the F1 scores for the interpolation-based, RMS, and NMI methods were 99.1%, 39.7%, and 75.9%, respectively. The interpolation-based method was also the most effective detection method for identifying poor channels in samples of real HD-EMG data. F1 scores for the detection of poor-quality channels in real data for the interpolation-based, RMS, and NMI methods were 96.4%, 64.5%, and 50.0%, respectively. Following the detection of poor-quality channels, 2D spline interpolation was used to successfully reconstruct these channels. Reconstruction of known target channels had a percent residual difference (PRD) of 15.5 ± 12.1%. The proposed interpolation-based method is an effective approach for the detection and reconstruction of poor-quality channels in HD-EMG. Full article

(This article belongs to the Special Issue EMG Sensors and Signal Processing Technologies)

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15 pages, 3794 KiB

Open AccessArticle

Interaction with a Hand Rehabilitation Exoskeleton in EMG-Driven Bilateral Therapy: Influence of Visual Biofeedback on the Users’ Performance

by Ana Cisnal, Paula Gordaliza, Javier Pérez Turiel and Juan Carlos Fraile

Sensors 2023, 23(4), 2048; https://doi.org/10.3390/s23042048 - 11 Feb 2023

Cited by 7 | Viewed by 2411

Abstract

The effectiveness of EMG biofeedback with neurorehabilitation robotic platforms has not been previously addressed. The present work evaluates the influence of an EMG-based visual biofeedback on the user performance when performing EMG-driven bilateral exercises with a robotic hand exoskeleton. Eighteen healthy subjects were asked to perform 1-min randomly generated sequences of hand gestures (rest, open and close) in four different conditions resulting from the combination of using or not (1) EMG-based visual biofeedback and (2) kinesthetic feedback from the exoskeleton movement. The user performance in each test was measured by computing similarity between the target gestures and the recognized user gestures using the L2 distance. Statistically significant differences in the subject performance were found in the type of provided feedback (p-value 0.0124). Pairwise comparisons showed that the L2 distance was statistically significantly lower when only EMG-based visual feedback was present (2.89 ± 0.71) than with the presence of the kinesthetic feedback alone (3.43 ± 0.75, p-value = 0.0412) or the combination of both (3.39 ± 0.70, p-value = 0.0497). Hence, EMG-based visual feedback enables subjects to increase their control over the movement of the robotic platform by assessing their muscle activation in real time. This type of feedback could benefit patients in learning more quickly how to activate robot functions, increasing their motivation towards rehabilitation. Full article

(This article belongs to the Special Issue EMG Sensors and Signal Processing Technologies)

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Review

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43 pages, 3864 KiB

Open AccessReview

Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review

by Amina Ben Haj Amor, Oussama El Ghoul and Mohamed Jemni

Sensors 2023, 23(19), 8343; https://doi.org/10.3390/s23198343 - 9 Oct 2023

Cited by 2 | Viewed by 2260

Abstract

The analysis and recognition of sign languages are currently active fields of research focused on sign recognition. Various approaches differ in terms of analysis methods and the devices used for sign acquisition. Traditional methods rely on video analysis or spatial positioning data calculated using motion capture tools. In contrast to these conventional recognition and classification approaches, electromyogram (EMG) signals, which measure muscle electrical activity, offer potential technology for detecting gestures. These EMG-based approaches have recently gained attention due to their advantages. This prompted us to conduct a comprehensive study on the methods, approaches, and projects utilizing EMG sensors for sign language handshape recognition. In this paper, we provided an overview of the sign language recognition field through a literature review, with the objective of offering an in-depth review of the most significant techniques. These techniques were categorized in this article based on their respective methodologies. The survey discussed the progress and challenges in sign language recognition systems based on surface electromyography (sEMG) signals. These systems have shown promise but face issues like sEMG data variability and sensor placement. Multiple sensors enhance reliability and accuracy. Machine learning, including deep learning, is used to address these challenges. Common classifiers in sEMG-based sign language recognition include SVM, ANN, CNN, KNN, HMM, and LSTM. While SVM and ANN are widely used, random forest and KNN have shown better performance in some cases. A multilayer perceptron neural network achieved perfect accuracy in one study. CNN, often paired with LSTM, ranks as the third most popular classifier and can achieve exceptional accuracy, reaching up to 99.6% when utilizing both EMG and IMU data. LSTM is highly regarded for handling sequential dependencies in EMG signals, making it a critical component of sign language recognition systems. In summary, the survey highlights the prevalence of SVM and ANN classifiers but also suggests the effectiveness of alternative classifiers like random forests and KNNs. LSTM emerges as the most suitable algorithm for capturing sequential dependencies and improving gesture recognition in EMG-based sign language recognition systems. Full article

(This article belongs to the Special Issue EMG Sensors and Signal Processing Technologies)

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29 pages, 947 KiB

Open AccessReview

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review

by Marianne Boyer, Laurent Bouyer, Jean-Sébastien Roy and Alexandre Campeau-Lecours

Sensors 2023, 23(6), 2927; https://doi.org/10.3390/s23062927 - 8 Mar 2023

Cited by 10 | Viewed by 7360

Abstract

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and artifacts, leading to potential data misinterpretation. Even assuming best practices, the acquired signal may still contain contaminants. The aim of this paper is to review methods employed to reduce the contamination of single channel EMG signals. Specifically, we focus on methods which enable a full reconstruction of the EMG signal without loss of information. This includes subtraction methods used in the time domain, denoising methods performed after the signal decomposition and hybrid approaches that combine multiple methods. Finally, this paper provides a discussion on the suitability of the individual methods based on the type of contaminant(s) present in the signal and the specific requirements of the application. Full article

(This article belongs to the Special Issue EMG Sensors and Signal Processing Technologies)

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