Special Issue "Wearable EMG Sensors for Smart Applications"

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensor and Bioelectronic Devices".

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editor

Dr. Ramana Kumar Vinjamuri
E-Mail Website
Guest Editor
Department of Computer Science and Electrical Engineering University of Maryland Baltimore County, Baltimore, MD 21250, USA
Interests: brain–computer interfaces; neuroprosthetics and exoskeletons; machine learning; signal processing

Special Issue Information

Dear Colleagues,

Electromyography (EMG) is used to show biological signals consisting of electrical activity produced by skeletal muscles. This has several applications in motor control, motor learning, biofeedback, biomechanics, neuromuscular physiology, movement disorders, and physical therapy. The clinical applications of EMG date back to as early as the1950s. Since then, several advancements have been made in the design and implementation of EMG sensors and, also, in post-processing and real-time processing of EMG signals. With these advancements, EMG has found applications in recent applications such as human–machine interfaces, prosthesis and exoskeleton control, powered wheelchair control, stress and fatigue measurements, and many other clinical applications. Fast forward to today’s applications, wearable EMG sensors are found everywhere in smart applications to improve health and wellbeing.   

The design and development of EMG sensors has resulted in their evolution over the years from being bulky wired cumbersome invasive electrodes to wireless wearable high-density noninvasive and completely safe sleeves with arrays of multiple electrodes. Meanwhile, the post-processing and real-time processing algorithms have seen developments with improved pattern recognition, thanks to recent advances in artificial intelligence, deep learning, machine learning, and signal processing. Consequently, with the coming of new age technologies and internet of things, several smart applications of EMG sensors have arisen in the areas of myoelectric control of robots and exoskeletons, clinical applications in telemedicine and telehealth, and wearable technologies to monitor everyday physical activity, stress, fatigue, and overall health and wellbeing.

The aim of this Special Issue is to compile the contributions of current leading researchers in the following areas: (1) the design and development of wearable EMG sensors; (2) the post-processing and real-time processing of EMG signals using artificial intelligence, deep learning, and machine learning; and (3) the smart applications of these wearable EMG sensors.

Dr. Ramana Kumar Vinjamuri
Guest Editor

Manuscript Submission Information

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Keywords

  • electromyography (EMG)
  • surface electromyogram (sEMG)
  • high-density surface EMG (HD-EMG)
  • wearable sensors
  • EMG feature extraction
  • EMG pattern recognition
  • gesture recognition
  • myoelectric control
  • prosthetics
  • exoskeletons
  • human–machine interfaces
  • machine learning
  • deep learning
  • signal processing
  • time–frequency analysis
  • smart applications
  • stress, fatigue and activity measurements

Published Papers (2 papers)

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Research

Article
An Ultra-Low Power Surface EMG Sensor for Wearable Biometric and Medical Applications
Biosensors 2021, 11(11), 411; https://0-doi-org.brum.beds.ac.uk/10.3390/bios11110411 - 21 Oct 2021
Viewed by 237
Abstract
In recent years, the surface electromyography (EMG) signal has received a lot of attention. EMG signals are used to analyze muscle activity or to evaluate a patient’s muscle status. However, commercial surface EMG systems are expensive and have high power consumption. Therefore, the [...] Read more.
In recent years, the surface electromyography (EMG) signal has received a lot of attention. EMG signals are used to analyze muscle activity or to evaluate a patient’s muscle status. However, commercial surface EMG systems are expensive and have high power consumption. Therefore, the purpose of this paper is to implement a surface EMG acquisition system that supports high sampling and ultra-low power consumption measurement. This work analyzes and optimizes each part of the EMG acquisition circuit and combines an MCU with BLE. Regarding the MCU power saving method, the system uses two different frequency MCU clock sources and we proposed a ping-pong buffer as the memory architecture to achieve the best power saving effect. The measured surface EMG signal samples can be forwarded immediately to the host for further processing and additional application. The results show that the average current of the proposed architecture can be reduced by 92.72% compared with commercial devices, and the battery life is 9.057 times longer. In addition, the correlation coefficients were up to 99.5%, which represents a high relative agreement between the commercial and the proposed system. Full article
(This article belongs to the Special Issue Wearable EMG Sensors for Smart Applications)
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Article
Neurophysiological Factors Affecting Muscle Innervation Zone Estimation Using Surface EMG: A Simulation Study
Biosensors 2021, 11(10), 356; https://0-doi-org.brum.beds.ac.uk/10.3390/bios11100356 - 27 Sep 2021
Viewed by 320
Abstract
Surface electromyography (EMG) recorded by a linear or 2-dimensional electrode array can be used to estimate the location of muscle innervation zones (IZ). There are various neurophysiological factors that may influence surface EMG and thus potentially compromise muscle IZ estimation. The objective of [...] Read more.
Surface electromyography (EMG) recorded by a linear or 2-dimensional electrode array can be used to estimate the location of muscle innervation zones (IZ). There are various neurophysiological factors that may influence surface EMG and thus potentially compromise muscle IZ estimation. The objective of this study was to evaluate how surface-EMG-based IZ estimation might be affected by different factors, including varying degrees of motor unit (MU) synchronization in the case of single or double IZs. The study was performed by implementing a model simulating surface EMG activity. Three different MU synchronization conditions were simulated, namely no synchronization, medium level synchronization, and complete synchronization analog to M wave. Surface EMG signals recorded by a 2-dimensional electrode array were simulated from a muscle with single and double IZs, respectively. For each situation, the IZ was estimated from surface EMG and compared with the one used in the model for performance evaluation. For the muscle with only one IZ, the estimated IZ location from surface EMG was consistent with the one used in the model for all the three MU synchronization conditions. For the muscle with double IZs, at least one IZ was appropriately estimated from interference surface EMG when there was no MU synchronization. However, the estimated IZ was different from either of the two IZ locations used in the model for the other two MU synchronization conditions. For muscles with a single IZ, MU synchronization has little effect on IZ estimation from electrode array surface EMG. However, caution is required for multiple IZ muscles since MU synchronization might lead to false IZ estimation. Full article
(This article belongs to the Special Issue Wearable EMG Sensors for Smart Applications)
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