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Electromyography (EMG) Signal Acquisition and Processing

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 40224

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Dr. Nebojsa Malesevic

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Guest Editor

Department of Biomedical Engineering, Faculty of Engineering, Lund University, 223 63 Lund, Sweden
Interests: electrical stimulation; sensory feedback; rehabilitation; motor control; biomedical signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Anders Björkman

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Guest Editor

Department of Hand Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, 413 45 Göteborg, Sweden
Interests: EMG signals

Special Issue Information

Dear Colleagues,

Electromyography (EMG) is a technique commonly used in evaluating patients for neuro-muscular system disorders, but can also be used for research and development such as for studying the underlying mechanisms of human movement or for devising an intuitive, natural-like control of prosthetic devices. To achieve clinical, research, and everyday life utility goals, the state-of-the-art in these research areas depends on EMG signal acquisition and processing methods. Integrated circuits that condition the input (analog) signal and sample it for digital signal processing are becoming available as standard electronic components, allowing for the design of custom, elaborate, multi-channel, and wearable EMG acquisition systems. Furthermore, machine learning algorithms, including deep learning, were originally developed for different research areas and have proved to be applicable for EMG-related problems, such as the assessment and classification of human limb movements.

This Special Issue addresses both EMG signal acquisition (electronics and electrodes) and processing techniques (analytic and machine learning), independently or jointly, employed within novel sensor solutions.

Potential topics include, but are not limited to:

EMG amplifiers;
EMG electrodes;
EMG signal digitalization;
Wearable EMG sensors;
EMG sensor applications;
Multi-channel and high-density EMG recording;
EMG signal characterization;
Decomposition of EMG signal into individual motor units;
Classification of human movements using EMG signals;
Evaluation of human movement using EMG signals;
Evaluation of the neuro-muscular system using EMG signals;
Regression of joint forces, joint kinematics, and joint kinetics using EMG signals.

Dr. Nebojsa Malesevic
Dr. Anders Björkman
Guest Editors

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Keywords

EMG sensors
EMG signals
wearables

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Published Papers (11 papers)

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26 pages, 1442 KiB

Open AccessArticle

On the Distribution of Muscle Signals: A Method for Distance-Based Classification of Human Gestures

by Jonas Große Sundrup and Katja Mombaur

Sensors 2023, 23(17), 7441; https://doi.org/10.3390/s23177441 - 26 Aug 2023

Cited by 1 | Viewed by 1408

Abstract

We investigate the distribution of muscle signatures of human hand gestures under Dynamic Time Warping. For this we present a k-Nearest-Neighbors classifier using Dynamic Time Warping for the distance estimate. To understand the resulting classification performance, we investigate the distribution of the recorded samples and derive a method of assessing the separability of a set of gestures. In addition to this, we present and evaluate two approaches with reduced real-time computational cost with regards to their effectiveness and the mechanics behind them. We further investigate the impact of different parameters with regards to practical usability and background rejection, allowing fine-tuning of the induced classification procedure. Full article

(This article belongs to the Special Issue Electromyography (EMG) Signal Acquisition and Processing)

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15 pages, 2451 KiB

Open AccessArticle

Analysis of Ankle Muscle Dynamics during the STS Process Based on Wearable Sensors

by Kun Liu, Shuo Ji, Yong Liu, Chi Gao, Shizhong Zhang, Jun Fu and Lei Dai

Sensors 2023, 23(14), 6607; https://doi.org/10.3390/s23146607 - 22 Jul 2023

Cited by 2 | Viewed by 1729

Abstract

Ankle joint moment is an important indicator for evaluating the stability of the human body during the sit-to-stand (STS) movement, so a method to analyze ankle joint moment is needed. In this study, a wearable sensor system that could derive surface-electromyography (sEMG) signals and kinematic signals on the lower limbs was developed for non-invasive estimation of ankle muscle dynamics during the STS movement. Based on the established ankle joint musculoskeletal information and sEMG signals, ankle joint moment during the STS movement was calculated. In addition, based on a four-segment STS dynamic model and kinematic signals, ankle joint moment during the STS movement was calculated using the inverse dynamics method. Ten healthy young people participated in the experiment, who wore a self-developed wearable sensor system and performed STS movements as an experimental task. The results showed that there was a high correlation (all R ≥ 0.88) between the results of the two methods for estimating ankle joint moment. The research in this paper can provide theoretical support for the development of an intelligent bionic joint actuator and clinical rehabilitation evaluation. Full article

(This article belongs to the Special Issue Electromyography (EMG) Signal Acquisition and Processing)

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14 pages, 3671 KiB

Open AccessArticle

Relationship between EMG and fNIRS during Dynamic Movements

by Natalia Daniel, Kamil Sybilski, Wojciech Kaczmarek, Dariusz Siemiaszko and Jerzy Małachowski

Sensors 2023, 23(11), 5004; https://doi.org/10.3390/s23115004 - 23 May 2023

Cited by 9 | Viewed by 3505

Abstract

In the scientific literature focused on surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS), which have been described together and separately many times, presenting different possible applications, researchers have explored a diverse range of topics related to these advanced physiological measurement techniques. However, the analysis of the two signals and their interrelationships continues to be a focus of study in both static and dynamic movements. The main purpose of this study was to determine the relationship between signals during dynamic movements. To carry out the analysis described, the authors of this research paper chose two sports exercise protocols: the Astrand–Rhyming Step Test and the Astrand Treadmill Test. In this study, oxygen consumption and muscle activity were recorded from the gastrocnemius muscle of the left leg of five female participants. This study found positive correlations between EMG and fNIRS signals in all participants: 0.343–0.788 (median-Pearson) and 0.192–0.832 (median-Spearman). On the treadmill, the signal correlations between the participants with the most active and least active lifestyle achieved the following medians: 0.788 (Pearson)/0.832 (Spearman) and 0.470 (Pearson)/0.406 (Spearman), respectively. The shapes of the changes in the EMG and fNIRS signals during exercise suggest a mutual relationship during dynamic movements. Furthermore, during the treadmill test, a higher correlation was observed between the EMG and NIRS signals in participants with a more active lifestyle. Due to the sample size, the results should be interpreted with caution. Full article

(This article belongs to the Special Issue Electromyography (EMG) Signal Acquisition and Processing)

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18 pages, 3322 KiB

Open AccessArticle

Recognition of Hand Gestures Based on EMG Signals with Deep and Double-Deep Q-Networks

by Ángel Leonardo Valdivieso Caraguay, Juan Pablo Vásconez, Lorena Isabel Barona López and Marco E. Benalcázar

Sensors 2023, 23(8), 3905; https://doi.org/10.3390/s23083905 - 12 Apr 2023

Cited by 12 | Viewed by 5845

Abstract

In recent years, hand gesture recognition (HGR) technologies that use electromyography (EMG) signals have been of considerable interest in developing human–machine interfaces. Most state-of-the-art HGR approaches are based mainly on supervised machine learning (ML). However, the use of reinforcement learning (RL) techniques to classify EMGs is still a new and open research topic. Methods based on RL have some advantages such as promising classification performance and online learning from the user’s experience. In this work, we propose a user-specific HGR system based on an RL-based agent that learns to characterize EMG signals from five different hand gestures using Deep Q-network (DQN) and Double-Deep Q-Network (Double-DQN) algorithms. Both methods use a feed-forward artificial neural network (ANN) for the representation of the agent policy. We also performed additional tests by adding a long–short-term memory (LSTM) layer to the ANN to analyze and compare its performance. We performed experiments using training, validation, and test sets from our public dataset, EMG-EPN-612. The final accuracy results demonstrate that the best model was DQN without LSTM, obtaining classification and recognition accuracies of up to

90.37 % \pm 10.7 %

and

82.52 % \pm 10.9 %

, respectively. The results obtained in this work demonstrate that RL methods such as DQN and Double-DQN can obtain promising results for classification and recognition problems based on EMG signals. Full article

(This article belongs to the Special Issue Electromyography (EMG) Signal Acquisition and Processing)

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22 pages, 7078 KiB

Open AccessArticle

Elbow Joint Stiffness Functional Scales Based on Hill’s Muscle Model and Genetic Optimization

by Marija Radmilović, Djordje Urukalo, Milica M. Janković, Suzana Dedijer Dujović, Tijana J. Dimkić Tomić, Maja Trumić and Kosta Jovanović

Sensors 2023, 23(3), 1709; https://doi.org/10.3390/s23031709 - 3 Feb 2023

Cited by 2 | Viewed by 3033

Abstract

The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified “Reach and retrieve” task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill’s muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (Stiffness) based on the estimated joint torque and the estimated elbow angle. Finally, novel Stiffness scales (general patterns) for upper limb functional assessment in the two performed test scenarios were proposed. The stiffness scales showed an exponentially increasing trend with increasing movement tempo, as well as with increasing weights. The obtained general Stiffness patterns from the group of participants without sensorimotor disorders could significantly contribute to the further monitoring of motor recovery in patients with sensorimotor disorders. Full article

(This article belongs to the Special Issue Electromyography (EMG) Signal Acquisition and Processing)

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18 pages, 16352 KiB

Open AccessArticle

A Novel Screen-Printed Textile Interface for High-Density Electromyography Recording

by Luis Pelaez Murciego, Abiodun Komolafe, Nikola Peřinka, Helga Nunes-Matos, Katja Junker, Ander García Díez, Senentxu Lanceros-Méndez, Russel Torah, Erika G. Spaich and Strahinja Dosen

Sensors 2023, 23(3), 1113; https://doi.org/10.3390/s23031113 - 18 Jan 2023

Cited by 8 | Viewed by 3437

Abstract

Recording electrical muscle activity using a dense matrix of detection points (high-density electromyography, EMG) is of interest in a range of different applications, from human-machine interfacing to rehabilitation and clinical assessment. The wider application of high-density EMG is, however, limited as the clinical interfaces are not convenient for practical use (e.g., require conductive gel/cream). In the present study, we describe a novel dry electrode (TEX) in which the matrix of sensing pads is screen printed on textile and then coated with a soft polymer to ensure good skin-electrode contact. To benchmark the novel solution, an identical electrode was produced using state-of-the-art technology (polyethylene terephthalate with hydrogel, PET) and a process that ensured a high-quality sample. The two electrodes were then compared in terms of signal quality as well as functional application. The tests showed that the signals collected using PET and TEX were characterised by similar spectra, magnitude, spatial distribution and signal-to-noise ratio. The electrodes were used by seven healthy subjects and an amputee participant to recognise seven hand gestures, leading to similar performance during offline analysis and online control. The comprehensive assessment, therefore, demonstrated that the proposed textile interface is an attractive solution for practical applications. Full article

(This article belongs to the Special Issue Electromyography (EMG) Signal Acquisition and Processing)

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14 pages, 3285 KiB

Open AccessArticle

Surface EMG Statistical and Performance Analysis of Targeted-Muscle-Reinnervated (TMR) Transhumeral Prosthesis Users in Home and Laboratory Settings

by Bingbin Wang, Levi Hargrove, Xinqi Bao and Ernest N. Kamavuako

Sensors 2022, 22(24), 9849; https://doi.org/10.3390/s22249849 - 14 Dec 2022

Cited by 1 | Viewed by 2211

Abstract

A pattern-recognition (PR)-based myoelectric control system is the trend of future prostheses development. Compared with conventional prosthetic control systems, PR-based control systems provide high dexterity, with many studies achieving >95% accuracy in the last two decades. However, most research studies have been conducted in the laboratory. There is limited research investigating how EMG signals are acquired when users operate PR-based systems in their home and community environments. This study compares the statistical properties of surface electromyography (sEMG) signals used to calibrate prostheses and quantifies the quality of calibration sEMG data through separability indices, repeatability indices, and correlation coefficients in home and laboratory settings. The results demonstrate no significant differences in classification performance between home and laboratory environments in within-calibration classification error (home: 6.33 ± 2.13%, laboratory: 7.57 ± 3.44%). However, between-calibration classification errors (home: 40.61 ± 9.19%, laboratory: 44.98 ± 12.15%) were statistically different. Furthermore, the difference in all statistical properties of sEMG signals is significant (p < 0.05). Separability indices reveal that motion classes are more diverse in the home setting. In summary, differences in sEMG signals generated between home and laboratory only affect between-calibration performance. Full article

(This article belongs to the Special Issue Electromyography (EMG) Signal Acquisition and Processing)

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25 pages, 52553 KiB

Open AccessArticle

Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides

by Alexey Anastasiev, Hideki Kadone, Aiki Marushima, Hiroki Watanabe, Alexander Zaboronok, Shinya Watanabe, Akira Matsumura, Kenji Suzuki, Yuji Matsumaru and Eiichi Ishikawa

Sensors 2022, 22(22), 8733; https://doi.org/10.3390/s22228733 - 11 Nov 2022

Cited by 16 | Viewed by 4660

Abstract

In clinical practice, acute post-stroke paresis of the extremities fundamentally complicates timely rehabilitation of motor functions; however, recently, residual and distorted musculoskeletal signals have been used to initiate feedback-driven solutions for establishing motor rehabilitation. Here, we investigate the possibilities of basic hand gesture recognition in acute stroke patients with hand paresis using a novel, acute stroke, four-component multidomain feature set (ASF-4) with feature vector weight additions (ASF-14NP, ASF-24P) and supervised learning algorithms trained only by surface electromyography (sEMG). A total of 19 (65.9 ± 12.4 years old; 12 men, seven women) acute stroke survivors (12.4 ± 6.3 days since onset) with hand paresis (Brunnstrom stage 4 ± 1/4 ± 1, SIAS 3 ± 1/3 ± 2, FMA-UE 40 ± 20) performed 10 repetitive hand movements reflecting basic activities of daily living (ADLs): rest, fist, pinch, wrist flexion, wrist extension, finger spread, and thumb up. Signals were recorded using an eight-channel, portable sEMG device with electrode placement on the forearms and thenar areas of both limbs (four sensors on each extremity). Using data preprocessing, semi-automatic segmentation, and a set of extracted feature vectors, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbors (k-NN) classifiers for statistical comparison and validity (paired t-tests, p-value < 0.05), we were able to discriminate myoelectrical patterns for each gesture on both paretic and non-paretic sides. Despite any post-stroke conditions, the evaluated total accuracy rate by the 10-fold cross-validation using SVM among four-, five-, six-, and seven-gesture models were 96.62%, 94.20%, 94.45%, and 95.57% for non-paretic and 90.37%, 88.48%, 88.60%, and 89.75% for paretic limbs, respectively. LDA had competitive results using PCA whereas k-NN was a less efficient classifier in gesture prediction. Thus, we demonstrate partial efficacy of the combination of sEMG and supervised learning for upper-limb rehabilitation procedures for early acute stroke motor recovery and various treatment applications. Full article

(This article belongs to the Special Issue Electromyography (EMG) Signal Acquisition and Processing)

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27 pages, 4490 KiB

Open AccessArticle

Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography

by Nebojsa Malesevic, Anders Björkman, Gert S. Andersson, Christian Cipriani and Christian Antfolk

Sensors 2022, 22(13), 5054; https://doi.org/10.3390/s22135054 - 5 Jul 2022

Cited by 4 | Viewed by 3236

Abstract

Although seemingly effortless, the control of the human hand is backed by an elaborate neuro-muscular mechanism. The end result is typically a smooth action with the precise positioning of the joints of the hand and an exerted force that can be modulated to enable precise interaction with the surroundings. Unfortunately, even the most sophisticated technology cannot replace such a comprehensive role but can offer only basic hand functionalities. This issue arises from the drawbacks of the prosthetic hand control strategies that commonly rely on surface EMG signals that contain a high level of noise, thus limiting accurate and robust multi-joint movement estimation. The use of intramuscular EMG results in higher quality signals which, in turn, lead to an improvement in prosthetic control performance. Here, we present the evaluation of fourteen common/well-known algorithms (mean absolute value, variance, slope sign change, zero crossing, Willison amplitude, waveform length, signal envelope, total signal energy, Teager energy in the time domain, Teager energy in the frequency domain, modified Teager energy, mean of signal frequencies, median of signal frequencies, and firing rate) for the direct and proportional control of a prosthetic hand. The method involves the estimation of the forces generated in the hand by using different algorithms applied to iEMG signals from our recently published database, and comparing them to the measured forces (ground truth). The results presented in this paper are intended to be used as a baseline performance metric for more advanced algorithms that will be made and tested using the same database. Full article

(This article belongs to the Special Issue Electromyography (EMG) Signal Acquisition and Processing)

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16 pages, 2611 KiB

Open AccessArticle

Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography

by Taichi Tanaka, Isao Nambu, Yoshiko Maruyama and Yasuhiro Wada

Sensors 2022, 22(13), 5005; https://doi.org/10.3390/s22135005 - 2 Jul 2022

Cited by 25 | Viewed by 4257

Abstract

Many researchers have used machine learning models to control artificial hands, walking aids, assistance suits, etc., using the biological signal of electromyography (EMG). The use of such devices requires high classification accuracy. One method for improving the classification performance of machine learning models is normalization, such as z-score. However, normalization is not used in most EMG-based motion prediction studies because of the need for calibration and fluctuation of reference value for calibration (cannot re-use). Therefore, in this study, we proposed a normalization method that combines sliding-window and z-score normalization that can be implemented in real-time processing without need for calibration. The effectiveness of this normalization method was confirmed by conducting a single-joint movement experiment of the elbow and predicting its rest, flexion, and extension movements from the EMG signal. The proposed method achieved 77.7% accuracy, an improvement of 21.5% compared to the non-normalization (56.2%). Furthermore, when using a model trained by other people’s data for application without calibration, the proposed method achieved 63.1% accuracy, an improvement of 8.8% compared to the z-score (54.4%). These results showed the effectiveness of the simple and easy-to-implement method, and that the classification performance of the machine learning model could be improved. Full article

(This article belongs to the Special Issue Electromyography (EMG) Signal Acquisition and Processing)

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11 pages, 886 KiB

Open AccessBrief Report

Crosstalk in Facial EMG and Its Reduction Using ICA

by Wataru Sato and Takanori Kochiyama

Sensors 2023, 23(5), 2720; https://doi.org/10.3390/s23052720 - 2 Mar 2023

Cited by 3 | Viewed by 2698

Abstract

There is ample evidence that electromyography (EMG) signals from the corrugator supercilii and zygomatic major muscles can provide valuable information for the assessment of subjective emotional experiences. Although previous research suggested that facial EMG data could be affected by crosstalk from adjacent facial muscles, it remains unproven whether such crosstalk occurs and, if so, how it can be reduced. To investigate this, we instructed participants (n = 29) to perform the facial actions of frowning, smiling, chewing, and speaking, in isolation and combination. During these actions, we measured facial EMG signals from the corrugator supercilii, zygomatic major, masseter, and suprahyoid muscles. We performed an independent component analysis (ICA) of the EMG data and removed crosstalk components. Speaking and chewing induced EMG activity in the masseter and suprahyoid muscles, as well as the zygomatic major muscle. The ICA-reconstructed EMG signals reduced the effects of speaking and chewing on zygomatic major activity, compared with the original signals. These data suggest that: (1) mouth actions could induce crosstalk in zygomatic major EMG signals, and (2) ICA can reduce the effects of such crosstalk. Full article

(This article belongs to the Special Issue Electromyography (EMG) Signal Acquisition and Processing)

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