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Biosensors
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Wearable EMG Sensors for Smart Applications
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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: closed (1 October 2022) | Viewed by 23583

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 Issues, Collections and Topics in MDPI journals

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

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 submissions that pass pre-check are 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. Biosensors is an international peer-reviewed open access monthly 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 2700 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
  • 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 (5 papers)

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Research

30 pages, 8750 KiB  
Article
How Does Lower Limb Respond to Unexpected Balance Perturbations? New Insights from Synchronized Human Kinetics, Kinematics, Muscle Electromyography (EMG) and Mechanomyography (MMG) Data
by Ringo Tang-Long Zhu, Pei-Zhao Lyu, Shuai Li, Cheuk Ying Tong, Yan To Ling and Christina Zong-Hao Ma
Biosensors 2022, 12(6), 430; https://doi.org/10.3390/bios12060430 - 18 Jun 2022
Cited by 7 | Viewed by 2958
Abstract
Making rapid and proper compensatory postural adjustments is vital to prevent falls and fall-related injuries. This study aimed to investigate how, especially how rapidly, the multiple lower-limb muscles and joints would respond to the unexpected standing balance perturbations. Unexpected waist-pull perturbations with small, [...] Read more.
Making rapid and proper compensatory postural adjustments is vital to prevent falls and fall-related injuries. This study aimed to investigate how, especially how rapidly, the multiple lower-limb muscles and joints would respond to the unexpected standing balance perturbations. Unexpected waist-pull perturbations with small, medium and large magnitudes were delivered to twelve healthy young adults from the anterior, posterior, medial and lateral directions. Electromyographical (EMG) and mechanomyographical (MMG) responses of eight dominant-leg muscles (i.e., hip abductor/adductors, hip flexor/extensor, knee flexor/extensor, and ankle dorsiflexor/plantarflexors) together with the lower-limb joint angle, moment, and power data were recorded. The onset latencies, time to peak, peak values, and/or rate of change of these signals were analyzed. Statistical analysis revealed that: (1) agonist muscles resisting the delivered perturbation had faster activation than the antagonist muscles; (2) ankle muscles showed the largest rate of activation among eight muscles following both anteroposterior and mediolateral perturbations; (3) lower-limb joint moments that complied with the perturbation had faster increase; and (4) larger perturbation magnitude tended to evoke a faster response in muscle activities, but not necessarily in joint kinetics/kinematics. These findings provided insights regarding the underlying mechanism and lower-limb muscle activities to maintain reactive standing balance in healthy young adults. Full article
(This article belongs to the Special Issue Wearable EMG Sensors for Smart Applications)
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20 pages, 5176 KiB  
Article
Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal
by Md Belal Bin Heyat, Faijan Akhtar, Syed Jafar Abbas, Mohammed Al-Sarem, Abdulrahman Alqarafi, Antony Stalin, Rashid Abbasi, Abdullah Y. Muaad, Dakun Lai and Kaishun Wu
Biosensors 2022, 12(6), 427; https://doi.org/10.3390/bios12060427 - 17 Jun 2022
Cited by 39 | Viewed by 6751
Abstract
In the modern world, wearable smart devices are continuously used to monitor people’s health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, [...] Read more.
In the modern world, wearable smart devices are continuously used to monitor people’s health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques. Full article
(This article belongs to the Special Issue Wearable EMG Sensors for Smart Applications)
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18 pages, 4405 KiB  
Article
A Differentiable Dynamic Model for Musculoskeletal Simulation and Exoskeleton Control
by Chao-Hung Kuo, Jia-Wei Chen, Yi Yang, Yu-Hao Lan, Shao-Wei Lu, Ching-Fu Wang, Yu-Chun Lo, Chien-Lin Lin, Sheng-Huang Lin, Po-Chuan Chen and You-Yin Chen
Biosensors 2022, 12(5), 312; https://doi.org/10.3390/bios12050312 - 9 May 2022
Cited by 7 | Viewed by 2771
Abstract
An exoskeleton, a wearable device, was designed based on the user’s physical and cognitive interactions. The control of the exoskeleton uses biomedical signals reflecting the user intention as input, and its algorithm is calculated as an output to make the movement smooth. However, [...] Read more.
An exoskeleton, a wearable device, was designed based on the user’s physical and cognitive interactions. The control of the exoskeleton uses biomedical signals reflecting the user intention as input, and its algorithm is calculated as an output to make the movement smooth. However, the process of transforming the input of biomedical signals, such as electromyography (EMG), into the output of adjusting the torque and angle of the exoskeleton is limited by a finite time lag and precision of trajectory prediction, which result in a mismatch between the subject and exoskeleton. Here, we propose an EMG-based single-joint exoskeleton system by merging a differentiable continuous system with a dynamic musculoskeletal model. The parameters of each muscle contraction were calculated and applied to the rigid exoskeleton system to predict the precise trajectory. The results revealed accurate torque and angle prediction for the knee exoskeleton and good performance of assistance during movement. Our method outperformed other models regarding the rate of convergence and execution time. In conclusion, a differentiable continuous system merged with a dynamic musculoskeletal model supported the effective and accurate performance of an exoskeleton controlled by EMG signals. Full article
(This article belongs to the Special Issue Wearable EMG Sensors for Smart Applications)
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15 pages, 7539 KiB  
Article
An Ultra-Low Power Surface EMG Sensor for Wearable Biometric and Medical Applications
by Yi-Da Wu, Shanq-Jang Ruan and Yu-Hao Lee
Biosensors 2021, 11(11), 411; https://doi.org/10.3390/bios11110411 - 21 Oct 2021
Cited by 13 | Viewed by 5593
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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13 pages, 3247 KiB  
Article
Neurophysiological Factors Affecting Muscle Innervation Zone Estimation Using Surface EMG: A Simulation Study
by Chengjun Huang, Maoqi Chen, Xiaoyan Li, Yingchun Zhang, Sheng Li and Ping Zhou
Biosensors 2021, 11(10), 356; https://doi.org/10.3390/bios11100356 - 27 Sep 2021
Cited by 5 | Viewed by 2391
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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