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Special Issue "EMG Sensors and Applications"

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

Deadline for manuscript submissions: 1 December 2019

Special Issue Editors

Guest Editor
Dr. Erik Scheme

Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada
Website | E-Mail
Interests: pattern recognition; machine learning; signal processing and control; human–machine interfaces; rehabilitation engineering; Internet of things
Guest Editor
Dr. Angkoon Phinyomark

Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada
Website | E-Mail
Interests: EMG signal processing; pattern recognition; myoelectric control; gait biomechanics; neuroimaging

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 1800 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 (4 papers)

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Research

Open AccessArticle
sEMG-Based Hand-Gesture Classification Using a Generative Flow Model
Sensors 2019, 19(8), 1952; https://doi.org/10.3390/s19081952
Received: 14 March 2019 / Revised: 13 April 2019 / Accepted: 21 April 2019 / Published: 25 April 2019
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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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Open AccessArticle
An Insulated Flexible Sensor for Stable Electromyography Detection: Application to Prosthesis Control
Sensors 2019, 19(4), 961; https://doi.org/10.3390/s19040961
Received: 11 January 2019 / Revised: 5 February 2019 / Accepted: 16 February 2019 / Published: 24 February 2019
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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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Open AccessArticle
Ultra-Low-Power Digital Filtering for Insulated EMG Sensing
Sensors 2019, 19(4), 959; https://doi.org/10.3390/s19040959
Received: 11 January 2019 / Revised: 14 February 2019 / Accepted: 23 February 2019 / Published: 24 February 2019
Cited by 1 | PDF Full-text (32388 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle
sEMG-Based Drawing Trace Reconstruction: A Novel Hybrid Algorithm Fusing Gene Expression Programming into Kalman Filter
Sensors 2018, 18(10), 3296; https://doi.org/10.3390/s18103296
Received: 11 August 2018 / Revised: 18 September 2018 / Accepted: 27 September 2018 / Published: 30 September 2018
PDF Full-text (4069 KB) | HTML Full-text | XML Full-text
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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