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Electromyography (EMG) Sensor and System
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Electromyography (EMG) Sensor and System

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 16739

Special Issue Editors

Prof. Dr. Vicente Diaz

E-Mail Website
Guest Editor
Mechanical Engineering Department, Universidad Carlos III de Madrid, Avd. De la Universidad, Madrid 28911, Spain
Interests: transports; vehicle dynamics engineering; end-of-life recycling; sensors and motorvehicle inspection
Dr. Ester Olmeda

E-Mail Website
Guest Editor
Mechanical Engineering Department, Universidad Carlos III de Madrid, Avd. De la Universidad, 28911 Madrid, Spain
Interests: vehicle dynamics; residual stress; ergonomics and machinery safety

Special Issue Information

Electromyography (EMG) is based on the measurement of the electrical activity of the muscles and nerves in the human body. The EMG electrical signal, acquired on the surface of the skin, is the result of muscle movement activity and provides a wealth of information on the movement generated.

This type of biosignal is widely used in a variety of fields of science. Periodic monitoring of EMG signals can be used to detect diseases and to prevent problems such as heart attacks or strokes. In addition, the study of the biomechanics of human movement and biometric identification may be useful in detecting neuromuscular disorders. The field of human–machine interaction (mechanical actuators) can also benefit from the use of biosignals, and so can the field of computing due to the development of muscle–computer interfaces (immersive environments, video games, electronic devices or the control of robotic devices or “bionic” limbs).

Given all the possibilities that EMG signal analysis has, it is essential to ensure that the data collected are reliable and representative of the electrical activity of the muscles. Furthermore, to perform this analysis, it is very important that the acquisition system for obtaining these myoelectric signals be efficient and reliable. Finally, in order to optimize resources, it would be highly advisable, as is the trend, that all the elements that configure the acquisition system are low-cost.

Prof. Dr. Vicente Diaz
Dr. Ester Olmeda
Guest Editors

Manuscript Submission Information

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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

  • EMG
  • electromyography
  • surface electromyography
  • biosensing
  • bio-instrumentation
  • monitoring
  • band-pass filters
  • electrodes
  • biosensor
  • low-cost sensors

Published Papers (4 papers)

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21 pages, 2345 KiB  
Article
A Study on sEMG-Based Motor Variability and Functional Connectivity of the Upper Limb Depending on Weight Distributions in a Handle of a Cordless Stick-Type Vacuum Cleaner
by Hayeon Yu, Eunchae Kang and Joonho Chang
Sensors 2022, 22(13), 4835; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134835 - 26 Jun 2022
Viewed by 1400
Abstract
This study investigated the muscle activities, motor variability, and functional connectivity of the upper limb as a function of weight distributions in a handle of a cordless stick-type vacuum cleaner. Eighteen female college students with experience of vacuum cleaner-use participated in testing. Five [...] Read more.
This study investigated the muscle activities, motor variability, and functional connectivity of the upper limb as a function of weight distributions in a handle of a cordless stick-type vacuum cleaner. Eighteen female college students with experience of vacuum cleaner-use participated in testing. Five handles with different centers of mass (CM) were prepared (centroid, top-rear, top-front, bottom-front, and bottom-rear), and electromyography for the muscles of the upper limb were measured during vacuuming. The results showed that the %MVC values of the Extensor Carpi Ulnaris (p = 0.0038) and Deltoid Middle (p = 0.0094) increased but that of the Biceps Brachii (p = 0.0001) decreased, as the CM moved from the top to bottom area of the handle. The motor variability of the Extensor Carpi Ulnaris (p = 0.0335) and Brachioradialis (p = 0.0394) significantly varied depending on the CM locations but failed to show significance in the post-hoc analyses. Lastly, the functional connectivity values of the muscle pairs such as the Extensor Carpi Ulnaris–Deltoid Middle (p = 0.0016), Extensor Carpi Ulnaris–Upper Trapezius (p = 0.0174), Brachioradialis–Biceps Brachii (p = 0.0356), and Biceps Brachii–Upper Trapezius (p = 0.0102) were significantly altered as a function of the CM locations. The lowest functional connectivity was found with the handle of which CM was at centroid. Full article
(This article belongs to the Special Issue Electromyography (EMG) Sensor and System)
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18 pages, 76945 KiB  
Article
Surface Electromyography Study Using a Low-Cost System: Are There Neck Muscles Differences When the Passenger Is Warned during an Emergency Braking Inside an Autonomous Vehicle?
by Silvia Santos-Cuadros, Sergio Fuentes del Toro, Ester Olmeda and José Luis San Román
Sensors 2021, 21(16), 5378; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165378 - 09 Aug 2021
Cited by 3 | Viewed by 1514
Abstract
Deaths and serious injuries caused by traffic accidents is a concerning public health problem. However, the problem can be mitigated by the Autonomous Emergency Braking (AEB) system, which can avoid the impact. The market penetration of AEB is exponentially growing, and non-impact situations [...] Read more.
Deaths and serious injuries caused by traffic accidents is a concerning public health problem. However, the problem can be mitigated by the Autonomous Emergency Braking (AEB) system, which can avoid the impact. The market penetration of AEB is exponentially growing, and non-impact situations are expected to become more frequent. Thus, new injury patterns must be analysed, and the neck is particularly sensitive to sudden acceleration changes. Abrupt braking would be enough to be a potential risk for cervical spine injury. There is controversy about whether or not there are differences in cervical behaviour depending on whether passengers are relaxed or contract their muscles before the imminent accident. In the present manuscript, 18 volunteers were subjected to two different levels of awareness during an emergency braking test. Cervical muscles (sternocleidomastoid and trapezius) were analysed by the sEMG signal captured by means of a low-cost system. The differences observed in the muscle response according to gender and age were notable when passengers are warned. Gender differences were more significant in the post-braking phase. When passengers were relaxed, subjects older than 35 registered higher sEMG values. Meanwhile, when passengers contract their muscles, subjects who were younger than or equal to 35 years old experienced an increment in the values of the sEMG signals. Full article
(This article belongs to the Special Issue Electromyography (EMG) Sensor and System)
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18 pages, 6423 KiB  
Article
A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG
by Yumiao Chen, Zhongliang Yang and Yangliang Wen
Sensors 2021, 21(2), 578; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020578 - 15 Jan 2021
Cited by 14 | Viewed by 5120
Abstract
Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from [...] Read more.
Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from non-paretic hand for bilateral training. A customization method of geometrical parameters of soft actuators was presented, and their structure was redesigned. Then, the corresponding pressure values of air-pump to generate different angles of actuators were determined to support four hand motions (extension, rest, spherical grip, and fist). A two-step hybrid model combining the neural network and the state exclusion algorithm was proposed to recognize four hand motions via sEMG signals from the healthy limb. Four subjects were recruited to participate in the experiments. The experimental results show that the pressure values for the four hand motions were about −2, 0, 40, and 70 KPa, and the hybrid model can yield a mean accuracy of 98.7% across four hand motions. It can be concluded that the novel SExoG system can mirror the hand motions of non-paretic hand with good performance. Full article
(This article belongs to the Special Issue Electromyography (EMG) Sensor and System)
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12 pages, 1544 KiB  
Letter
An sEMG-Controlled 3D Game for Rehabilitation Therapies: Real-Time Time Hand Gesture Recognition Using Deep Learning Techniques
by Nadia Nasri, Sergio Orts-Escolano and Miguel Cazorla
Sensors 2020, 20(22), 6451; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226451 - 12 Nov 2020
Cited by 47 | Viewed by 5282
Abstract
In recent years the advances in Artificial Intelligence (AI) have been seen to play an important role in human well-being, in particular enabling novel forms of human-computer interaction for people with a disability. In this paper, we propose a sEMG-controlled 3D game that [...] Read more.
In recent years the advances in Artificial Intelligence (AI) have been seen to play an important role in human well-being, in particular enabling novel forms of human-computer interaction for people with a disability. In this paper, we propose a sEMG-controlled 3D game that leverages a deep learning-based architecture for real-time gesture recognition. The 3D game experience developed in the study is focused on rehabilitation exercises, allowing individuals with certain disabilities to use low-cost sEMG sensors to control the game experience. For this purpose, we acquired a novel dataset of seven gestures using the Myo armband device, which we utilized to train the proposed deep learning model. The signals captured were used as an input of a Conv-GRU architecture to classify the gestures. Further, we ran a live system with the participation of different individuals and analyzed the neural network’s classification for hand gestures. Finally, we also evaluated our system, testing it for 20 rounds with new participants and analyzed its results in a user study. Full article
(This article belongs to the Special Issue Electromyography (EMG) Sensor and System)
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