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Journal articles on the topic 'EMG-electromyography'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Marras, William S. "Industrial electromyography (EMG)." International Journal of Industrial Ergonomics 6, no. 1 (1990): 89–93. http://dx.doi.org/10.1016/0169-8141(90)90054-6.

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2

Cui, Chunhai, Enqian Xin, Meili Qu, and Shuai Jiang. "Fatigue and Abnormal State Detection by Using EMG Signal During Football Training." International Journal of Distributed Systems and Technologies 12, no. 2 (2021): 13–23. http://dx.doi.org/10.4018/ijdst.2021040102.

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This paper proposes to monitor and recognize the fatigue state during football training by analyzing the surface electromyography (EMG) signals. The surface electromyography (EMG) signal is closely connected with the state during sports and training. First, power frequency interference, motion artifacts, and baseline drift in the surface electromyography (EMG) signal are removed; second, the authors extract 6 features: rectified average value (ARV), integrated electromyography myoelectric value (IEMG), root mean square of electromyography value (RMS), median frequency (MF), average power frequ

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3

De Luca, Carlo J. "The Use of Surface Electromyography in Biomechanics." Journal of Applied Biomechanics 13, no. 2 (1997): 135–63. http://dx.doi.org/10.1123/jab.13.2.135.

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This lecture explores the various uses of surface electromyography in the field of biomechanics. Three groups of applications are considered: those involving the activation timing of muscles, the force/EMG signal relationship, and the use of the EMG signal as a fatigue index. Technical considerations for recording the EMG signal with maximal fidelity are reviewed, and a compendium of all known factors that affect the information contained in the EMG signal is presented. Questions are posed to guide the practitioner in the proper use of surface electromyography. Sixteen recommendations are made

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Bing, Chu Yih, S.Parasuraman, and M. K. A. Ahmed Khan. "Electromyography (EMG) and Human Locomotion." Procedia Engineering 41 (2012): 486–92. http://dx.doi.org/10.1016/j.proeng.2012.07.202.

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5

Solzi, P., and M. Lotem. "Malingering detected by electromyography (EMG)." Electroencephalography and Clinical Neurophysiology 75 (January 1990): S142. http://dx.doi.org/10.1016/0013-4694(90)92236-p.

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6

Daube, J. R. "Electromyography in CNS Disorders: Central EMG." Neurology 35, no. 2 (1985): 289. http://dx.doi.org/10.1212/wnl.35.2.289.

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7

Sasso, F., CG Stief, G. Gulino, et al. "Progress in corpus cavernosum electromyography (CC-EMG)–Third international workshop on corpus cavernosum electromyography (CC-EMG)." International Journal of Impotence Research 9, no. 1 (1997): 43–45. http://dx.doi.org/10.1038/sj.ijir.3900255.

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Marras, William S. "Overview of Electromyography in Ergonomics." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 30 (2000): 5–534. http://dx.doi.org/10.1177/154193120004403037.

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Electromyography (EMG) is a tool that can be very valuable in ergonomic studies if it is used correctly and if the associated limitations are appreciated. An understanding of the use of EMG transcends many areas of knowledge including physiology, instrumentation, recording technology, and signal processing and analysis. This paper provides a general overview of these areas so that an appreciation for how these areas interact and impact on the effective use of EMG.

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Kehri, Vikram, and Awale R. N. "EMG Signal Analysis for Diagnosis of Muscular Dystrophy Using Wavelet Transform, SVM and ANN." Biomedical and Pharmacology Journal 11, no. 3 (2018): 1583–91. http://dx.doi.org/10.13005/bpj/1525.

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Implementation of Artificial intelligence techniques is used as a medical diagnostic tool to increase the diagnostic accuracy and provide more additional knowledge. Muscular dystrophy is a disorder which diagnosed with Electromyography (EMG) signals. A Wavelet-based decomposition technique is proposed here to classified Healthy EMG signals (Normal) from abnormal muscular dystrophy EMG signals. In this work, a wavelet transform is applied to preprocessed EMG signals for decomposing it into different frequency sub-bands. Statistical analysis is carried out to these decomposed sub-bands to extrac

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10

Tang, Guojun. "Wearable Electronics for Surface and Needle Electromyography Measurements." Highlights in Science, Engineering and Technology 45 (April 18, 2023): 69–74. http://dx.doi.org/10.54097/hset.v45i.7310.

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Because the electromyography (EMG) signal can reflect the potential of muscle contraction controlled by neural activities, it has aroused great interest from researchers. As a result, a number of studies have been conducted around EMG. EMG-based wearable electronics are used not only in the diagnosis of neurogenic or myogenic diseases but also in sports science, rehabilitation, augmented reality, and virtual reality. This article briefly introduces the development history and background of EMG technology, then focuses on the classification of EMG and its technical mechanisms and reviews repres

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Kang, Hong, Wei Wang, Xin Li, Hongliang Du, Lan Li, and Jieyu Ma. "MULTICHANNEL s-EMG SYSTEM OF MASTICATORY MUSCLES: DESIGN AND CLINICAL APPLICATION IN DIAGNOSIS OF DYSFUNCTION IN STOMATOGNATHIC SYSTEM." Biomedical Engineering: Applications, Basis and Communications 27, no. 01 (2015): 1550008. http://dx.doi.org/10.4015/s1016237215500088.

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To develop a more convenient electromyography (EMG) signal acquisition system for masticatory muscles (MMs) will have implication for the diagnosis of disease of stomatological system and functional reconstruction in dentistry. This study attempted to design a multi-channel MM surface electromyography (s-EMG) signal acquisition system which has high common mode rejection ratio (CMRR) and preamplifier system. In this system, a USB graphical interface technology on windows operating system was specifically designed and s-EMG data were analyzed by a combination of time domain, frequency domain, t

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12

Lv, Shanshan, and Yanyu Dong. "ANALYSIS OF DIFFERENT INJURIES OF BASKETBALL PLAYERS BASED ON SURFACE ELECTROMYOGRAPHY." Revista Brasileira de Medicina do Esporte 27, spe2 (2021): 23–26. http://dx.doi.org/10.1590/1517-8692202127022020_0027.

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ABSTRACT Assessing the performance of basketball players is very important in the implementation of technical training programs. The application of electromyography (EMG) in basketball players is still relatively small. The evaluation of athletes’ muscle state index by EMG can guide sports training. This study used surface electromyography to test and compare EMG data, analyze muscle discharge timing, contribution rate and integral EMG value of the turning movement, aiming to explore the prevention mechanism of different types of injury affecting basketball players. The synchronous measurement

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13

Khaleel Awsaj, Mahmood, and Rabah Nory Farhan. "Intelligent System for Electromyography (EMG) Signals Classification." Journal of Engineering and Applied Sciences 14, no. 4 (2019): 1564–70. http://dx.doi.org/10.36478/jeasci.2019.1564.1570.

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14

Serej, Michał, and Maria Skublewska - Paszkowska. "The methods of EMG data processing." Journal of Computer Sciences Institute 3 (March 30, 2017): 38–45. http://dx.doi.org/10.35784/jcsi.591.

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The article presents both the methods of data processing of electromyography (EMG), and EMG signal analysis using the implemented piece of software. This application is used to load the EMG signal stored in a file with the .C3D extension. The analysis was conducted in terms of the highest muscles activaton during exercise recorded with Motion Capture technique.

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15

Samman, A., and V. A. Shakhnov. "Virtual Reality Mobile Platform for Restoring Upper Limbs Functions using Electromiography Data." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 3 (136) (September 2021): 84–99. http://dx.doi.org/10.18698/0236-3933-2021-3-84-99.

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The article describes a mobile virtual reality platform based on the biological feedback of electromyography for restoring the functions of the upper limbs of people affected by accidents, stroke, Parkinson's disease or who suffered as a result of military operations. The definition of the electromyography (EMG) signal is given. The effectiveness of the biological feedback method in the rehabilitation process is indicated. The problem of initial data preprocessing is considered in order to identify the informative features of the EMG signal in the time domain. The general scheme of a mobile vi

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Khalifa, Yaser E., Mohamed Hamed, Mohammed Anter Abdelhameed, et al. "Hip abductor dysfunction following total hip arthroplasty by modified direct lateral approach: Assessment by quantitative electromyography." Egyptian Orthopaedic Journal 58, no. 4 (2023): 295–304. http://dx.doi.org/10.4103/eoj.eoj_99_23.

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Abstract Background The use of the direct lateral approach and its modifications for total hip arthroplasty (THA) may lead to postoperative abductor weakness. Assessment of abductor muscle function by the use of quantitative electromyography (EMG) aims to investigate the nature of abductor muscle dysfunction. Methods We conducted a study on 40 patients who had hip replacement through the modified direct lateral approach. EMG was performed before surgery on the affected and normal sides and repeated on the operated side after surgery by 6 and 12 weeks. Analysis of EMG of the three abductor musc

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Nishi, Shamima Easmin, Rehana Basri, and Mohammad Khursheed Alam. "Uses of electromyography in dentistry: An overview with meta-analysis." European Journal of Dentistry 10, no. 03 (2016): 419–25. http://dx.doi.org/10.4103/1305-7456.184156.

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ABSTRACT Objective: The purpose of this study was to review the uses of electromyography (EMG) in dentistry in the last few years in related research. EMG is an advanced technique to record and evaluate muscle activity. In the previous days, EMG was only used for medical sciences, but now EMG playing a tremendous role in medical as well as dental sector. Materials and Methods: Several electronic databases such as Google Scholar, PubMed, Science Direct, and Web of Science were systematically searched for studies published until July 2015. Results: EMG can be used in both diagnosis and treatment

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Oo, Nandar, Hla Myo Tun, Devasis Pradhan, Lei Lei Yin Win, Mya Mya Aye, and Thandar Oo. "Implementation of the Process for Contamination in Electromyography (EMG) Signal by Using Noise Removal Techniques." Journal of Novel Engineering Science and Technology 3, no. 03 (2024): 94–98. https://doi.org/10.56741/jnest.v3i03.627.

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The paper describes the analysis of electromyography (EMG) signals using noise removal techniques. The problem in this study is to consider a noise removal technique for basic EMG signal processing by the Band Pass Filter method. A research approach to designing simulation codes for observing EMG signal modeling and noise removal techniques through mathematical methods from signals and systems concepts. The results confirm that it can provide high-performance target monitoring of the EMG signal in real-world applications.

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PHINYOMARK, ANGKOON, PORNCHAI PHUKPATTARANONT, CHUSAK LIMSAKUL, and MONTRI PHOTHISONOTHAI. "ELECTROMYOGRAPHY (EMG) SIGNAL CLASSIFICATION BASED ON DETRENDED FLUCTUATION ANALYSIS." Fluctuation and Noise Letters 10, no. 03 (2011): 281–301. http://dx.doi.org/10.1142/s0219477511000570.

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Electromyography (EMG) signal is a useful signal in various medical and engineering applications. To extract the useful information in the EMG signal, feature extraction method should be performed. The extracted features of the EMG signal are usually calculated based on linear or statistical methods, but the EMG signal exhibits the nonlinear and more complex in the properties. With recent advances in nonlinear analysis we are proposing the study of the EMG signals from upper-limb movements using Detrended Fluctuation Analysis (DFA) method. This study used EMG signals obtained from eight upper-

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20

Seliverstova, E. G., M. V. Sinkin, A. Y. Kordonsky, and A. A. Grin. "Methodology of electromyography of the lumbar paraspinal muscles." Medical alphabet 1, no. 22 (2023): 29–34. http://dx.doi.org/10.33667/2078-5631-2023-22-29-34.

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Electromyography (EMG) of the lumbar paraspinal muscles (PM) is performed for differential diagnosis of lumbosacral radiculopathy and other proximal peripheral nerve injuries such as lumbosacral plexopathy or sciatic neuropathy. In neurosurgery, EMG of the lumbar PM can clarify the level of the compressed spinal nerve root in polyradiculopathy due to degenerative spinal deasese. In this article we describe in detail the technique of the study, present the factors limiting the use of EMG in the diagnosis of radiculopathy and determination of its anatomical level.

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21

Palermo, Andrea, Gerardo Cazzato, Irma Trilli, et al. "The use of cranial electromyography in athletes." Oral & Implantology 16, no. 3.1suppl (2024): 506–21. https://doi.org/10.11138/oi163.1suppl506-521.

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Cranial electromyography (EMG) is an advanced diagnostic tool that provides valuable insights into the functioning of the stomatognathic system, which includes the jaws, teeth, and related musculature. This review explores the growing application of cranial EMG in sports medicine, particularly in athletes, focusing on its role in assessing neuromuscular balance and identifying underlying issues that may impact performance and overall musculoskeletal health. The stomatognathic system plays a critical role in maintaining postural stability, coordination, and strength, all of which are essential

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22

Phillips, David Alan, Angelic Rose Del Vecchio, Kevin Carroll, and Evan Lee Matthews. "Developing a Practical Application of the Isometric Squat and Surface Electromyography." Biomechanics 1, no. 1 (2021): 145–51. http://dx.doi.org/10.3390/biomechanics1010011.

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Electromyography (EMG) is a research tool used in gait analysis, muscle coordination evaluation, clinical evaluation and sports techniques. Electromyography can provide an insight into neural adaptations, cross education effects, bilateral contraction deficiencies, and antagonist activity in exercise-related movements. While there are clear benefits to using EMG in exercise-related professions, accessibility, cost, and difficulty interpreting the data limit its use in strength and clinical settings. We propose a practical EMG assessment using the isometric squat to identify compensatory activa

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23

Phillips, David, and Andrew Karduna. "Deltoid Electromyography is Reliable During Submaximal Isometric Ramp Contractions." Journal of Applied Biomechanics 33, no. 3 (2017): 237–40. http://dx.doi.org/10.1123/jab.2016-0224.

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The EMG and load relationship is commonly measured with multiple submaximal isometric contractions. This method is both time consuming and may introduce fatigue. The purpose of this study was to determine if the electromyography (EMG) amplitude from the middle deltoid was reliable during isometric ramp contractions (IRCs) at different angles of elevation and rates of force application. Surface EMG was measured at 3 shoulder elevation angles during IRCs at 4 submaximal levels of maximum voluntary contraction (MVC). Data were reliable in all conditions except during the rate relative to the subj

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Zinvi Fu. "OPTIMIZING SURFACE ELECTROMYOGRAPHY ACQUISITION WITHOUT RIGHT LEG DRIVE CIRCUIT." International Journal of Engineering Science Technologies 1, no. 1 (2019): 13–19. http://dx.doi.org/10.29121/ijoest.v1.i1.2017.02.

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The right leg drive (RLD) is a circuit associated with electrocardiography acquisition circuits. For electromyography (EMG), the RLD circuit is used to a lesser degree. In general, the RLD circuit provides better noise reduction. This study compares the output of the EMG with and without the RLD circuit. The results indicate that with a good filter design, the direct grounding method can match the RLD in terms of noise reduction. As a result, EMG application, the RLD drive can be omitted.

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Zinvi, Fu, Bani Hashim A.Y., Jamaludin Z., S. Mohamad I., and Nasir N. "OPTIMIZING SURFACE ELECTROMYOGRAPHY ACQUISITION WITHOUT RIGHT LEG DRIVE CIRCUIT." International Journal of Engineering Science Technologies 1, no. 1 (2017): 13–19. https://doi.org/10.5281/zenodo.273463.

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The right leg drive (RLD) is a circuit associated with electrocardiography acquisition circuits. For electromyography (EMG), the RLD circuit is used to a lesser degree. In general, the RLD circuit provides better noise reduction. This study compares the output of the EMG with and without the RLD circuit. The results indicate that with a good filter design, the direct grounding method can match the RLD in terms of noise reduction. As a result, EMG application, the RLD drive can be omitted.

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Kokesh, John, Lawrence R. Robinson, Paul W. Flint, and Charles W. Cummings. "Correlation between Stroboscopy and Electromyography in Laryngeal Paralysis." Annals of Otology, Rhinology & Laryngology 102, no. 11 (1993): 852–57. http://dx.doi.org/10.1177/000348949310201105.

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Twenty patients with vocal fold motion impairment were reviewed to correlate the findings of electromyography (EMG) and stroboscopy. The causes of motion impairment were idiopathic, previous surgery with recurrent laryngeal nerve injury, neck and skull base trauma, and neoplasm. The EMG studies were analyzed to assess the status of innervation of the immobile vocal fold. The presence or absence of the mucosal wave prior to therapeutic intervention was determined with stroboscopic examination. Eight of 10 patients with EMG evidence of reinnervation or partial denervation were found to have muco

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Omar, Siti Nashayu. "Application of digital signal processing and machine learning for Electromyography: A review." Asian Journal Of Medical Technology 1, no. 1 (2021): 30–45. http://dx.doi.org/10.32896/ajmedtech.v1n1.30-45.

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This paper reviewed the Application of Digital Signal Processing (DPS) and Machine Learning (ML) for Electromyography (EMG) by previous studies. There is a need of the DSP and ML application into the EMG study to classify the signal in order to minimize the EMG noise of signal and the EMG signal characteristic. The common techniques analysis of signal processing is disccussed and compared to identify the best techniques used in order to process from raw data of EMG signal info EMG signal analysis, then some types of machine learning is discussed to identify which types of machine learning have

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Al-Khazzar, Ahmed M., Zainab Altaweel, and Jabbar S. Hussain. "Using deep neural networks in classifying electromyography signals for hand gestures." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 217–27. https://doi.org/10.11591/ijai.v13.i1.pp217-227.

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Electromyography (EMG) signals are used for various applications, especially in smart prostheses. Recognizing various gestures (hand movements) in EMG systems introduces challenges. These challenges include the noise effect on EMG signals and the difficulty in identifying the exact movement from the collected EMG data amongst others. In this paper, three neural networkmodels are trained using an open EMG dataset to classify and recognize seven different gestures based on the collected EMG data. The three implemented models are: a four-layer deep neural network (DNN), an eight-layer DNN, and a

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DaSalla, C., J. Kim, and Y. Koike. "Robot Control Using Electromyography (EMG) Signals of the Wrist." Applied Bionics and Biomechanics 2, no. 2 (2005): 97–102. http://dx.doi.org/10.1155/2005/952754.

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The aim of this paper is to design a human–interface system, using EMG signals elicited by various wrist movements, to control a robot. EMG signals are normalized and based on joint torque. A three-layer neural network is used to estimate posture of the wrist and forearm from EMG signals. After training the neural network and obtaining appropriate weights, the subject was able to control the robot in real time using wrist and forearm movements.

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Kjelland, James M. "Application of Electromyography and Electromyographic Biofeedback in Music Performance Research: A Review of the Literature since 1985." Medical Problems of Performing Artists 15, no. 3 (2000): 115–18. http://dx.doi.org/10.21091/mppa.2000.3023.

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The diagnostic application of electromyography (EMG) and the combination of EMG and clinical biofeedback (EMG-BF) have been shown to be effective in the assessment and management of muscle tension in a wide range of activities, including sports as well as music. However, not all applications of EMG-BF have been found to be successful; indeed, some of the early reports of the benefits have been debated and disputed since the earliest applications of the technology. It is the purpose of this paper to glean a profile of research applying EMG and EMG-BF to music performance since approximately 198

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Lukyanchikov, Andrei, Alexei Melnikov, and Oleg Lukyanchikov. "Algorithms for classification of a single channel EMG signal for human-computer interaction." ITM Web of Conferences 18 (2018): 02001. http://dx.doi.org/10.1051/itmconf/20181802001.

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One of the most accurate and effective ways to control gestures is to control muscle activity, which occurs with any movement. Electromyography (EMG) is used to record such activity. This article compares SVM classification algorithms, perceptron, random trees and the method of density of probability in relation to the EMG signal. Arduino Leonardo with a single-channel Shield EMG is used to record the signal. The aim of this paper is to prove the possibility of creating a cheap and accessible biointerface based on EMG signal.

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Aratow, M., R. E. Ballard, A. G. Crenshaw, et al. "Intramuscular pressure and electromyography as indexes of force during isokinetic exercise." Journal of Applied Physiology 74, no. 6 (1993): 2634–40. http://dx.doi.org/10.1152/jappl.1993.74.6.2634.

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A direct method for measuring force production of specific muscles during dynamic exercise is presently unavailable. Previous studies indicate that both intramuscular pressure (IMP) and electromyography (EMG) correlate linearly with muscle contraction force during isometric exercise. The objective of this study was to compare IMP and EMG as linear assessors of muscle contraction force during dynamic exercise. IMP and surface EMG activity were recorded during concentric and eccentric isokinetic plantarflexion and dorsiflexion of the ankle joint from the tibialis anterior (TA) and soleus (SOL) m

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Lee, Kyeongjin. "EMG-Triggered Pedaling Training on Muscle Activation, Gait, and Motor Function for Stroke Patients." Brain Sciences 12, no. 1 (2022): 76. http://dx.doi.org/10.3390/brainsci12010076.

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This study aimed to determine the effects of electromyography (EMG)-triggered pedaling training to improve motor functions in the lower extremities, muscle activation, gait, postural balance, and activities of daily living in stroke patients. Subjects were randomly allocated to two groups: the EMG-triggered pedaling training group (EMG-PTG, n = 21) and the traditional pedaling training group (TPTG, n = 20). Both groups trained five times per week for four weeks, with 50 min per session. Lower extremity motor function was assessed using the Fugl–Meyer Assessment (FMA). Muscle activation of the

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Hochreiter, Jakob, Eric Hoche, Luisa Janik, et al. "Machine-Learning-Based Detecting of Eyelid Closure and Smiling Using Surface Electromyography of Auricular Muscles in Patients with Postparalytic Facial Synkinesis: A Feasibility Study." Diagnostics 13, no. 3 (2023): 554. http://dx.doi.org/10.3390/diagnostics13030554.

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Surface electromyography (EMG) allows reliable detection of muscle activity in all nine intrinsic and extrinsic ear muscles during facial muscle movements. The ear muscles are affected by synkinetic EMG activity in patients with postparalytic facial synkinesis (PFS). The aim of the present work was to establish a machine-learning-based algorithm to detect eyelid closure and smiling in patients with PFS by recording sEMG using surface electromyography of the auricular muscles. Sixteen patients (10 female, 6 male) with PFS were included. EMG acquisition of the anterior auricular muscle, superior

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Ahsan, Prof Dr Md Shamim, and Kazi Mahmud Hasan. "DESIGN AND DEVELOPMENT OF AN ELECTROMYOGRAPHY SENSOR ACTUATED PROSTHETIC ARM." Latin American Applied Research - An international journal 52, no. 3 (2022): 191–200. http://dx.doi.org/10.52292/j.laar.2022.817.

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Replacement of missing arms by an active prosthetic arm can improve the quality of life of an amputee. We demonstrate the development of a prosthetic arm by analyzing the electromyography (EMG) signals received from the EMG sensor connected to the muscles of a cleft arm to support disabled people. The prosthetic arm was designed using SolidWorks simulator. After convincing simulation results, we developed the prosthetic arm using 3D printing technology. The robust prosthetic arm was made of polymer for light weight and long durability. We analyzed the EMG signals of different muscles such as f

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Al-Khazzar, Ahmed, Zainab Altaweel, and Jabbar Salman Hussain. "Using deep neural networks in classifying electromyography signals for hand gestures." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 217. http://dx.doi.org/10.11591/ijai.v13.i1.pp217-227.

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<span lang="EN-US">Electromyography (EMG) signals are used for various applications, especially in smart prostheses. Recognizing various gestures (hand movements) in EMG systems introduces challenges. These challenges include the noise effect on EMG signals and the difficulty in identifying the exact movement from the collected EMG data amongst others. In this paper, three neural network models are trained using an open EMG dataset to classify and recognize seven different gestures based on the collected EMG data. The three implemented models are: a four-layer deep neural network (DNN),

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Zohirov, K., S. Boykobilov, M. Temirov, M. Sattorov, and F. Ruziboev. "Analytical review of methods for recording and classifying movements based on electromyography." Международный Журнал Теоретических и Прикладных Вопросов Цифровых Технологий 8, no. 1 (2025): 175–82. https://doi.org/10.62132/ijdt.v8i1.246.

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This paper provides a comprehensive overview of optimal methods and processes for recording, processing, and classifying electromyography (EMG) signals in the context of human movement rehabilitation. It begins by exploring advanced techniques for accurate and noise-free EMG signal acquisition, emphasizing the importance of electrode placement, signal amplification, and filtering strategies. The paper then delves into modern signal processing methods, such as feature extraction and dimensionality reduction, which enhance the interpretability of EMG data. Furthermore, the study highlights cutti

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Norali, A. N., M. N. Anas, Z. Zakaria, M. Asymawi, A. H. Abu Bakar, and Y. F. Chong. "Electromyography Signal Pattern Recognition for Movement of Shoulder." Journal of Physics: Conference Series 2071, no. 1 (2021): 012049. http://dx.doi.org/10.1088/1742-6596/2071/1/012049.

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Abstract Pectoralis major and deltoid are two muscles that are associated with the movement of the shoulder. Electromyography (EMG) signal acquired from these two muscles can be used to classify the movement of the shoulder based on pattern recognition. In this paper, an experiment for EMG data collection involves eight healthy male subjects who perform four shoulder movements which are flexion, extension, internal rotation and external rotation. Feature extraction of EMG data is done using root mean square (RMS), variance (VAR) and zero crossing (ZC). For pattern recognition, the classifiers

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Oo, Thandar, and Pornchai Phukpattaranont. "Signal-to-Noise Ratio Estimation in Electromyography Signals Contaminated with Electrocardiography Signals." Fluctuation and Noise Letters 19, no. 03 (2020): 2050027. http://dx.doi.org/10.1142/s0219477520500273.

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When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used

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Jones, N. B., S. Q. Wang, and A. S. Sehmi. "Evaluation of Three Quantitative Approaches in Electromyography (EMG)." IFAC Proceedings Volumes 26, no. 2 (1993): 283–86. http://dx.doi.org/10.1016/s1474-6670(17)48732-5.

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Aarås, Arne, and Ola Ro. "Electromyography (EMG) — Methodology and application in occupational health." International Journal of Industrial Ergonomics 20, no. 3 (1997): 207–14. http://dx.doi.org/10.1016/s0169-8141(96)00052-2.

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Boige, N., L. M. N. Mashako, G. Cargill, J. P. Cezard, and J. Navarro. "26 INTEREST OF COLONIC ELECTROMYOGRAPHY (EMG) IN CHILDREN." Pediatric Research 24, no. 3 (1988): 409. http://dx.doi.org/10.1203/00006450-198809000-00049.

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Karim Awan, Usman, and Salman Hussain. "Workers Ergonomics Measures Enhancement Through Surface Electromyography (EMG)." International journal of Engineering Works 10, no. 01 (2023): 01–09. http://dx.doi.org/10.34259/ijew.23.10020109.

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B. Radeef, Farah, and Basma A. Faihan. "DESIGN AND VALIDATION OF A LOW−COST WIRELESS ELECTROMYOGRAPHY SYSTEM." iraq journal of market research and consumer protection 14, no. 1 (2022): 36–44. http://dx.doi.org/10.28936/jmracpc14.1.2022.(4).

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Electromyography (EMG) is being explored for evaluating muscle activity. For gait analysis, EMG needs to be small, lightweight, portable device, and with low power consumption. The proposed superficial EMG (sEMG) system is aimed to be used in rehabilitation centers and biomechanics laboratories for gait analysis in Iraq. The system is built using MyoWare, which is controlled by using STM32F100 microcontroller. The sEMG signal is transferred via Bluetooth to the computer (about 30m range) for further processing. MATLAB is used for sEMG signal conditioning. The overall system cost (without compu

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MULDAYANI, WAHYU, ARIZAL MUJIBTAMALA NANDA IMRON, KHAIRUL ANAM, SUMARDI SUMARDI, WIDJONARKO WIDJONARKO, and ZILVANHISNA EMKA FITRI. "Pengenalan Pola Sinyal Electromyography (EMG) pada Gerakan Jari Tangan Kanan." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 8, no. 3 (2020): 591. http://dx.doi.org/10.26760/elkomika.v8i3.591.

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ABSTRAKSinyal EMG merupakan salah satu sinyal yang dapat digunakan untuk memberikan perintah pada kursi roda listrik. Sinyal EMG yang digunakan diambil dari sinyal otot fleksor dan ekstensor yang berada di tangan kanan. Sinyal tersebut diambil menggunakan sensor Myo Armband. Klasifikasi sinyal EMG diambil dari pergerakan jari yang mewakili perintah gerak yaitu jari kelingking untuk bergerak maju, jari manis untuk berhenti, jari tengah untuk belok kanan dan jari telunjuk untuk belok kiri. Setiap sinyal EMG diekstraksi fitur untuk menentukan karakteristik sinyal sehingga fitur yang diperoleh ada

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Anas Fouad Ahmed. "A quick survey of filtering techniques for surface electromyography signals." Global Journal of Engineering and Technology Advances 11, no. 3 (2022): 105–10. http://dx.doi.org/10.30574/gjeta.2022.11.3.0101.

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Electromyography (EMG) represents the electrical activity of muscles, and it has a wide range of usage in biomedical and clinical tasks. During myoelectrical stimulation, the EMG signal has two sources: the meaningful electrical response of the muscles and signal noise. Technical noise (such as power line noise) and biological noise (ECG). The noises in the system must be efficiently rejected, as this will disturb the analysis of the activity of the muscle. This paper presents different types of noise that corrupt the EMG signal and the main denoising approaches for minimizing the noise effect

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Philothra, Petrina Theda, Sri Mardjiati Mei Wulan, and Ratna Darjanti Haryadi. "Electromyography reveals the etiology of floppy infant in developing country." Romanian Journal of Neurology 22, no. 4 (2023): 336–39. http://dx.doi.org/10.37897/rjn.2023.4.9.

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Background. Floppy infants are correlated with the extensive differential diagnosis. It can make diagnostic approach become more challenging. Case report. We report a 10-months-old infant referred from Pediatrician to electromyography (EMG) laboratory presenting with floppy and developmental delays. The central and motor neuron manifestations also increased of CPK levels bring ambiguity for the diagnosis. EMG may distinguish the cause from myopathy, anterior horn cell, neuromuscular junction (NMJ) or central origin when genetic testing is not routinely done in developing country. Conclusion. E

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Anas, Fouad Ahmed. "A quick survey of filtering techniques for surface electromyography signals." Global Journal of Engineering and Technology Advances 11, no. 3 (2022): 105–10. https://doi.org/10.5281/zenodo.6962096.

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Electromyography (EMG) represents the electrical activity of muscles, and it has a wide range of usage in biomedical and clinical tasks. During myoelectrical stimulation, the EMG signal has two sources: the meaningful electrical response of the muscles and signal noise. Technical noise (such as power line noise) and biological noise (ECG). The noises in the system must be efficiently rejected, as this will disturb the analysis of the activity of the muscle. This paper presents different types of noise that corrupt the EMG signal and the main denoising approaches for minimizing the noise effect

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RABBI, MOHAMMAD FAZLE, KAMARUL HAWARI GHAZALI, OMAR ALTWIJRI, et al. "SIGNIFICANCE OF ELECTROMYOGRAPHY IN THE ASSESSMENT OF DIABETIC NEUROPATHY." Journal of Mechanics in Medicine and Biology 19, no. 03 (2019): 1930001. http://dx.doi.org/10.1142/s0219519419300011.

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Diabetic neuropathy is one of the physical complications of diabetes mellitus (DM) patients with a long history of diabetes. An electromyography (EMG)-based assessment may be very useful for the management of diabetic neuropathy. In the present study, we aimed to summarize all of the findings and recommendations obtained from previous studies that investigated the application of EMG to the assessment of diabetic neuropathy. An extensive search of the prominent electronic databases PubMed, Google Scholar and Scopus was performed to evaluate the following areas: (i) what are the muscles to be ev

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PARASURAMAN, S., and ARIF WICAKSONO OYONG. "ROBOT-ASSISTED STROKE REHABILITATION: JOINT TORQUE/FORCE CONVERSION FROM EMG USING GA PROCESS." Journal of Mechanics in Medicine and Biology 11, no. 04 (2011): 827–43. http://dx.doi.org/10.1142/s0219519411003880.

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This project focuses on the development of robot-assisted stroke rehabilitation by implementing electromyography (EMG) as the interface between robot and user communication. The key issue in the implementation of EMG in this application is the conversion of EMG signal into torque data. This article presents a methodology of EMG signal to estimated joint torque conversion by using genetic algorithm (GA). The basic principle of GA, formulation, and implementation to the problem are discussed in this article. Experimentation with real-life EMG data has been carried out to assess the feasibility o

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