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EMG Sensors and Applications
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EMG Sensors and Applications

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

Deadline for manuscript submissions: closed (1 December 2019) | Viewed by 69312

Special Issue Editors

Dr. Erik Scheme

E-Mail Website
Guest Editor
Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada
Interests: pattern recognition; machine learning; signal processing and control; human–machine interfaces; time–frequency analysis; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Angkoon Phinyomark

E-Mail Website
Guest Editor
Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada
Interests: EMG signal processing; myoelectric control; pattern recognition; machine learning; gait biomechanics; neuroimaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The electromyogram (EMG) signal is a biological signal produced by muscles throughout the human body when contracted and represents neuromuscular activity. Impressive advancements have been made in EMG signal processing and pattern recognition over the past several decades. This has greatly increased the number of potential applications for the use of EMG, including but not limited to, powered upper-limb prostheses, electric power wheelchairs, human-computer-interactions, and diagnoses in clinical applications.

In early works, a common approach to measuring EMG signals, known as sparse multi-channel surface EMG, required placing electrodes precisely over specific muscles. To facilitate EMG-based interfaces for everyday use, however, their use should be simple and non-invasive, such as a watch, an armband, jewellery, or concealed beneath clothing. More recently, EMG sensors have been positioned more generally, such as radially around the circumference of a flexible band (e.g., EMG armbands and high-density surface EMG grids (HD-EMG)). Due to the recent development of these sensors, together with advances in wireless communication and embedded computing technologies, EMG data can indeed now be obtained unobtrusively using wearable EMG devices.

EMG data collected from these different classes of surface EMG sensors have been analysed in both the temporal and spatial domains, leading to advances based on novel signal processing and machine learning techniques. For example, HD-EMG can be viewed as an EMG image, and thus can be analysed using image processing techniques and deep learning (as exemplified by a convolutional neural network) approaches.

The aim of this Special Issue is to bring together leading active researchers in the development of EMG sensors and their applications. Works on innovative EMG signal processing and machine learning algorithms aimed at addressing critical issues related to this new generation of EMG sensors are also encouraged.

Dr. Erik Scheme
Dr. Angkoon Phinyomark
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

  • Electromyography (EMG)
  • Surface electromyogram (sEMG)
  • High-density surface EMG (HD-EMG)
  • Wearable sensors
  • EMG feature extraction
  • EMG pattern recognition
  • Gesture recognition
  • Muscle-computer interface
  • Myoelectric control
  • Prosthetics

Published Papers (7 papers)

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Research

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16 pages, 5934 KiB  
Article
Validation of a Low-Cost Electromyography (EMG) System via a Commercial and Accurate EMG Device: Pilot Study
by Sergio Fuentes del Toro, Yuyang Wei, Ester Olmeda, Lei Ren, Wei Guowu and Vicente Díaz
Sensors 2019, 19(23), 5214; https://doi.org/10.3390/s19235214 - 28 Nov 2019
Cited by 31 | Viewed by 11194
Abstract
Electromyography (EMG) devices are well-suited for measuring the behaviour of muscles during an exercise or a task, and are widely used in many different research areas. Their disadvantage is that commercial systems are expensive. We designed a low-cost EMG system with enough accuracy [...] Read more.
Electromyography (EMG) devices are well-suited for measuring the behaviour of muscles during an exercise or a task, and are widely used in many different research areas. Their disadvantage is that commercial systems are expensive. We designed a low-cost EMG system with enough accuracy and reliability to be used in a wide range of possible ways. The present article focuses on the validation of the low-cost system we designed, which is compared with a commercially available, accurate device. The evaluation was done by means of a set of experiments, in which volunteers performed isometric and dynamic exercises while EMG signals from the rectus femoris muscle were registered by both the proposed low-cost system and a commercial system simultaneously. Analysis and assessment of three indicators to estimate the similarity between both signals were developed. These indicated a very good result, with spearman’s correlation averaging above 0.60, the energy ratio close to the 80% and the linear correlation coefficient approximating 100%. The agreement between both systems (custom and commercial) is excellent, although there are also some limitations, such as the delay of the signal (<1 s) and noise due to the hardware and assembly in the proposed system. Full article
(This article belongs to the Special Issue EMG Sensors and Applications)
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24 pages, 13117 KiB  
Article
A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition
by Ulysse Côté-Allard, Gabriel Gagnon-Turcotte, François Laviolette and Benoit Gosselin
Sensors 2019, 19(12), 2811; https://doi.org/10.3390/s19122811 - 24 Jun 2019
Cited by 52 | Viewed by 11229
Abstract
Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the [...] Read more.
Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost (∼150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly ( p < 0.05 ) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository. Full article
(This article belongs to the Special Issue EMG Sensors and Applications)
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16 pages, 8608 KiB  
Article
sEMG-Based Hand-Gesture Classification Using a Generative Flow Model
by Wentao Sun, Huaxin Liu, Rongyu Tang, Yiran Lang, Jiping He and Qiang Huang
Sensors 2019, 19(8), 1952; https://doi.org/10.3390/s19081952 - 25 Apr 2019
Cited by 17 | Viewed by 6449
Abstract
Conventional pattern-recognition algorithms for surface electromyography (sEMG)-based hand-gesture classification have difficulties in capturing the complexity and variability of sEMG. The deep structures of deep learning enable the method to learn high-level features of data to improve both accuracy and robustness of a classification. [...] Read more.
Conventional pattern-recognition algorithms for surface electromyography (sEMG)-based hand-gesture classification have difficulties in capturing the complexity and variability of sEMG. The deep structures of deep learning enable the method to learn high-level features of data to improve both accuracy and robustness of a classification. However, the features learned through deep learning are incomprehensible, and this issue has precluded the use of deep learning in clinical applications where model comprehension is required. In this paper, a generative flow model (GFM), which is a recent flourishing branch of deep learning, is used with a SoftMax classifier for hand-gesture classification. The proposed approach achieves 63.86 ± 5.12 % accuracy in classifying 53 different hand gestures from the NinaPro database 5. The distribution of all 53 hand gestures is modelled by the GFM, and each dimension of the feature learned by the GFM is comprehensible using the reverse flow of the GFM. Moreover, the feature appears to be related to muscle synergy to some extent. Full article
(This article belongs to the Special Issue EMG Sensors and Applications)
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32 pages, 41206 KiB  
Article
An Insulated Flexible Sensor for Stable Electromyography Detection: Application to Prosthesis Control
by Theresa Roland, Kerstin Wimberger, Sebastian Amsuess, Michael Friedrich Russold and Werner Baumgartner
Sensors 2019, 19(4), 961; https://doi.org/10.3390/s19040961 - 24 Feb 2019
Cited by 26 | Viewed by 13477
Abstract
Electromyography (EMG), the measurement of electrical muscle activity, is used in a variety of applications, including myoelectric upper-limb prostheses, which help amputees to regain independence and a higher quality of life. The state-of-the-art sensors in prostheses have a conductive connection to the skin [...] Read more.
Electromyography (EMG), the measurement of electrical muscle activity, is used in a variety of applications, including myoelectric upper-limb prostheses, which help amputees to regain independence and a higher quality of life. The state-of-the-art sensors in prostheses have a conductive connection to the skin and are therefore sensitive to sweat and require preparation of the skin. They are applied with some pressure to ensure a conductive connection, which may result in pressure marks and can be problematic for patients with circulatory disorders, who constitute a major group of amputees. Due to their insulating layer between skin and sensor area, capacitive sensors are insensitive to the skin condition, they require neither conductive connection to the skin nor electrolytic paste or skin preparation. Here, we describe a highly stable, low-power capacitive EMG measurement set-up that is suitable for real-world application. Various flexible multi-layer sensor set-ups made of copper and insulating foils, flex print and textiles were compared. These flexible sensor set-ups adapt to the anatomy of the human forearm, therefore they provide high wearing comfort and ensure stability against motion artifacts. The influence of the materials used in the sensor set-up on the magnitude of the coupled signal was demonstrated based on both theoretical analysis and measurement.The amplifier circuit was optimized for high signal quality, low power consumption and mobile application. Different shielding and guarding concepts were compared, leading to high SNR. Full article
(This article belongs to the Special Issue EMG Sensors and Applications)
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24 pages, 32563 KiB  
Article
Ultra-Low-Power Digital Filtering for Insulated EMG Sensing
by Theresa Roland, Sebastian Amsuess, Michael F. Russold and Werner Baumgartner
Sensors 2019, 19(4), 959; https://doi.org/10.3390/s19040959 - 24 Feb 2019
Cited by 21 | Viewed by 6926
Abstract
Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by state-of-the-art electromyography sensors, which use a conductive connection to the skin and are therefore sensitive to sweat. They are applied with some pressure to ensure [...] Read more.
Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by state-of-the-art electromyography sensors, which use a conductive connection to the skin and are therefore sensitive to sweat. They are applied with some pressure to ensure a conductive connection, which may result in pressure marks and can be problematic for patients with circulatory disorders, who constitute a major group of amputees. Here, we present ultra-low-power digital signal processing algorithms for an insulated EMG sensor which couples the EMG signal capacitively. These sensors require neither conductive connection to the skin nor electrolytic paste or skin preparation. Capacitive sensors allow straightforward application. However, they make a sophisticated signal amplification and noise suppression necessary. A low-cost sensor has been developed for real-time myoelectric prostheses control. The major hurdles in measuring the EMG are movement artifacts and external noise. We designed various digital filters to attenuate this noise. Optimal system setup and filter parameters for the trade-off between attenuation of this noise and sufficient EMG signal power for high signal quality were investigated. Additionally, an algorithm for movement artifact suppression, enabling robust application in real-world environments, is presented. The algorithms, which require minimal calculation resources and memory, are implemented on an ultra-low-power microcontroller. Full article
(This article belongs to the Special Issue EMG Sensors and Applications)
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20 pages, 4069 KiB  
Article
sEMG-Based Drawing Trace Reconstruction: A Novel Hybrid Algorithm Fusing Gene Expression Programming into Kalman Filter
by Zhongliang Yang, Yangliang Wen and Yumiao Chen
Sensors 2018, 18(10), 3296; https://doi.org/10.3390/s18103296 - 30 Sep 2018
Cited by 2 | Viewed by 3110
Abstract
How to reconstruct drawing and handwriting traces from surface electromyography (sEMG) signals accurately has attracted a number of researchers recently. An effective algorithm is crucial to reliable reconstruction. Previously, nonlinear regression methods have been utilized successfully to some extent. In the quest to [...] Read more.
How to reconstruct drawing and handwriting traces from surface electromyography (sEMG) signals accurately has attracted a number of researchers recently. An effective algorithm is crucial to reliable reconstruction. Previously, nonlinear regression methods have been utilized successfully to some extent. In the quest to improve the accuracy of transient myoelectric signal decoding, a novel hybrid algorithm KF-GEP fusing Gene Expression Programming (GEP) into Kalman Filter (KF) framework is proposed for sEMG-based drawing trace reconstruction. In this work, the KF-GEP was applied to reconstruct fourteen drawn shapes and ten numeric characters from sEMG signals across five participants. Then the reconstruction performance of KF-GEP, KF and GEP were compared. The experimental results show that the KF-GEP algorithm performs best because it combines the advantages of KF and GEP. The findings add to the literature on the muscle-computer interface and can be introduced to many practical fields. Full article
(This article belongs to the Special Issue EMG Sensors and Applications)
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Review

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36 pages, 570 KiB  
Review
Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review
by Andrés Jaramillo-Yánez, Marco E. Benalcázar and Elisa Mena-Maldonado
Sensors 2020, 20(9), 2467; https://doi.org/10.3390/s20092467 - 27 Apr 2020
Cited by 130 | Viewed by 12249
Abstract
Today, daily life is composed of many computing systems, therefore interacting with them in a natural way makes the communication process more comfortable. Human–Computer Interaction (HCI) has been developed to overcome the communication barriers between humans and computers. One form of HCI is [...] Read more.
Today, daily life is composed of many computing systems, therefore interacting with them in a natural way makes the communication process more comfortable. Human–Computer Interaction (HCI) has been developed to overcome the communication barriers between humans and computers. One form of HCI is Hand Gesture Recognition (HGR), which predicts the class and the instant of execution of a given movement of the hand. One possible input for these models is surface electromyography (EMG), which records the electrical activity of skeletal muscles. EMG signals contain information about the intention of movement generated by the human brain. This systematic literature review analyses the state-of-the-art of real-time hand gesture recognition models using EMG data and machine learning. We selected and assessed 65 primary studies following the Kitchenham methodology. Based on a common structure of machine learning-based systems, we analyzed the structure of the proposed models and standardized concepts in regard to the types of models, data acquisition, segmentation, preprocessing, feature extraction, classification, postprocessing, real-time processing, types of gestures, and evaluation metrics. Finally, we also identified trends and gaps that could open new directions of work for future research in the area of gesture recognition using EMG. Full article
(This article belongs to the Special Issue EMG Sensors and Applications)
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