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Journal articles on the topic 'EMG control'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Adila Ferdiansyah, Faizal, Prawito Prajitno, and Sastra Kusuma Wijaya. "EEG-EMG based bio-robotics elbow orthotics control." Journal of Physics: Conference Series 1528 (April 2020): 012033. http://dx.doi.org/10.1088/1742-6596/1528/1/012033.

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2

Awari, Rohan, Prof R. B. Kakkeri, and Radha Chande. "EMG Based Robot Control." IJIREEICE 7, no. 5 (2019): 14–16. http://dx.doi.org/10.17148/ijireeice.2019.7504.

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3

Gordleeva, S. Yu, S. A. Lobov, V. I. Mironov, et al. "DEVELOPMENT OF THE HARDWARE AND SOFTWARE COMPLEX CONTROLLING ROBOTIC DEVICES BY MEANS OF BIOELECTRIC SIGNALS OF THE BRAIN AND MUSCLES." Science and Innovations in Medicine 1, no. 3 (2016): 77–82. http://dx.doi.org/10.35693/2500-1388-2016-0-3-77-82.

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Aim - to develop a hardware-software complex with combined command-proportional control of robotic devices based on electromyography (EMG) and electroencephalography (EEG) signals. Materials and methods. EMG and EEG signals are recorded using our original units. The system also supports a number of commercial EEG and EMG recording systems, such as NVX52 (MCS ltd, Russia), DELSYS Trigno (Delsys Inc, USA), MYO Thalmic (Thalmic Labs, Canada). Raw signals undergo preprocessing and feature extraction. Then features are fed to classifiers. The interpretation unit controls robotic devices on the base

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4

Bortel, Radoslav, and Pavel Sovka. "EEG–EMG coherence enhancement." Signal Processing 86, no. 7 (2006): 1737–51. http://dx.doi.org/10.1016/j.sigpro.2005.09.011.

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5

Abdullah, Saad, Muhammad A. Khan, Mauro Serpelloni, and Emilio Sardini. "Hybrid EEG-EMG Based Brain Computer Interface (BCI) System For Real-Time Robotic Arm Control." Advanced Materials Letters 10, no. 1 (2018): 35–40. http://dx.doi.org/10.5185/amlett.2019.2171.

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6

Li, Chengze. "Leveraging EMG Signals for Precision Control." Applied and Computational Engineering 99, no. 1 (2024): 76–82. http://dx.doi.org/10.54254/2755-2721/99/20251766.

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This study explores the application of electromyography (EMG) signals for controlling mechanical systems using an Arduino Uno microcontroller, an Olimex EMG shield, sEMG electrodes, and a servo motor. EMG signals have been used for various applications such as prosthetics and assistive devices, proving to be a reliable source for control mechanisms. The experiment involves initial EMG testing, calculating RMS voltage, and integrating servo motor control. Results show that EMG signals can effectively control a servo motor, with improvements achieved through filtering, PID control, and multi-mot

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7

Kim, Sehyeon, Dae Youp Shin, Taekyung Kim, Sangsook Lee, Jung Keun Hyun, and Sung-Min Park. "Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography." Sensors 22, no. 2 (2022): 680. http://dx.doi.org/10.3390/s22020680.

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Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements

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8

Danna-Dos-Santos, Alessander, Tjeerd W. Boonstra, Adriana M. Degani, et al. "Multi-muscle control during bipedal stance: an EMG–EMG analysis approach." Experimental Brain Research 232, no. 1 (2013): 75–87. http://dx.doi.org/10.1007/s00221-013-3721-z.

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9

KARIS, MOHD SAFIRIN, HYREIL ANUAR KASDIRIN, NORAFIZAH ABAS, WIRA HIDAYAT MOHD SAAD, and MOHD SHAHRIEEL MOHD ARAS. "EMG BASED CONTROL OF WRIST EXOSKELETON." IIUM Engineering Journal 24, no. 2 (2023): 391–406. http://dx.doi.org/10.31436/iiumej.v24i2.2804.

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The significance of human motion intentions in a designed exoskeleton wrist control hand is essential for stroke survivors, thus making EMG signals an integral part of the overall system is critically important. However, EMG is a nonlinear signal that is easily influenced by several errors from its surroundings and certain of its applications require close monitoring to provide decent outcomes. Hence, this paper proposes to establish the relationship between EMG signals and wrist joint angle to estimate the desired wrist velocity. Fuzzy logic has been selected to form a dynamic modelling of wr

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10

Wheeler, K. R., M. H. Chang, and K. H. Knuth. "Gesture-based control and EMG decomposition." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 36, no. 4 (2006): 503–14. http://dx.doi.org/10.1109/tsmcc.2006.875418.

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11

Glaros, Alan G., and Karen Hanson. "EMG biofeedback and discriminative muscle control." Biofeedback and Self-Regulation 15, no. 2 (1990): 135–43. http://dx.doi.org/10.1007/bf00999144.

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12

Harver, Andrew, Joyce Segreto, and Harry Kotses. "EMG stability as a biofeedback control." Biofeedback and Self-Regulation 17, no. 2 (1992): 159–64. http://dx.doi.org/10.1007/bf01000107.

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13

Chan, F. H. Y., Yong-Sheng Yang, F. K. Lam, Yuan-Ting Zhang, and P. A. Parker. "Fuzzy EMG classification for prosthesis control." IEEE Transactions on Rehabilitation Engineering 8, no. 3 (2000): 305–11. http://dx.doi.org/10.1109/86.867872.

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14

Block, Susan, Mark Onslow, Rachael Roberts, and Samantha White. "Control of stuttering with EMG feedback." Advances in Speech Language Pathology 6, no. 2 (2004): 100–106. http://dx.doi.org/10.1080/14417040410001708521.

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15

ISHII, Chiharu. "Control of an Electric Wheelchair Based on EMG, EOG and EEG." Journal of the Japan Society for Precision Engineering 83, no. 11 (2017): 1006–9. http://dx.doi.org/10.2493/jjspe.83.1006.

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16

Liang, Peiyu. "Integration of Electroencephalography with Electromyography and Wearable Devices: A Concept Based on Experimental Investigations Utilizing Electromyogram Signals for Motor Function Control." Theoretical and Natural Science 84, no. 1 (2025): 51–57. https://doi.org/10.54254/2753-8818/2025.21232.

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The utilization of bioelectrical signals, including Electromyography (EMG) and Electroencephalography (EEG), has substantially contributed to advancements in assistive technologies, medical diagnostics, and rehabilitation practices. However, research that combines these two technologies is still relatively scarce. This study focuses on exploring the integration of EMG and EEG signals for controlling robots, particularly for assisting elderly and disabled individuals in performing daily tasks. The experiment involved collecting hand muscle signals using surface EMG (sEMG) electrodes, an Arduino

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17

Artemiadis, Panagiotis K., and Kostas J. Kyriakopoulos. "An EMG-Based Robot Control Scheme Robust to Time-Varying EMG Signal Features." IEEE Transactions on Information Technology in Biomedicine 14, no. 3 (2010): 582–88. http://dx.doi.org/10.1109/titb.2010.2040832.

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18

R., Aishwarya, Prabhu M., Sumithra G., and Anusiya M. "FEATURE EXTRACTION FOR EMG BASED PROSTHESES CONTROL." ICTACT Journal on Soft Computing 03, no. 02 (2013): 472–77. http://dx.doi.org/10.21917/ijsc.2013.0071.

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19

Kageyama, Yoshiyuki, Kiyoyuki Yamazaki, and Kiyotaka Hoshiai. "Robot Arm Control by Selectively Generated EMG." Journal of Robotics and Mechatronics 5, no. 3 (1993): 302–5. http://dx.doi.org/10.20965/jrm.1993.p0302.

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20

Raghavan, Anjali, and Sunny Joseph. "EMG analysis and control of artificial arm." International Journal on Cybernetics & Informatics 5, no. 2 (2016): 317–27. http://dx.doi.org/10.5121/ijci.2016.5234.

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21

Lenzi, T., S. M. M. De Rossi, N. Vitiello, and M. C. Carrozza. "Intention-Based EMG Control for Powered Exoskeletons." IEEE Transactions on Biomedical Engineering 59, no. 8 (2012): 2180–90. http://dx.doi.org/10.1109/tbme.2012.2198821.

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22

Oweis, Rami J., Remal Rihani, and Afnan Alkhawaja. "ANN-based EMG classification for myoelectric control." International Journal of Medical Engineering and Informatics 6, no. 4 (2014): 365. http://dx.doi.org/10.1504/ijmei.2014.065442.

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23

Fitzgerald, James. "IS051 HIGH RESOLUTION EMG-BASED PROSTHETIC CONTROL." Neuromodulation: Technology at the Neural Interface 28, no. 1 (2025): S28. https://doi.org/10.1016/j.neurom.2024.09.060.

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24

Marquez-Figueroa, Sandra, Yuriy S. Shmaliy, and Oscar Ibarra-Manzano. "Improving Gaussianity of EMG Envelope for Myoelectric Robot Arm Control." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 18 (August 5, 2021): 106–12. http://dx.doi.org/10.37394/23208.2021.18.12.

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Several methods have been developed in biomedical signal processing to extract the envelope and features of electromyography (EMG) signals and predict human motion. Also, efforts were made to use this information to improve the interaction of a human body and artificial protheses. The main operations here are envelope acquiring, artifacts filtering, estimate smoothing, EMG value standardizing, feature classifying, and motion recognizing. In this paper, we employ EMG data to extract the envelope with a highest Gaussianity using the rectified signal, where we deal with the absolute EMG signals s

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25

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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26

Corrêa, Janaina Maria Xavier, Raquel Vieira Niella, Jéssica Natália Silva de Oliveira, et al. "Analgesic effect of epidural maropitant and the combination of maropitant and lidocaine in cats subjected to ovariohysterectomy." Research, Society and Development 11, no. 7 (2022): e17511729612. http://dx.doi.org/10.33448/rsd-v11i7.29612.

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The present study aimed to evaluate the efficacy of epidural maropitant administered with or without lidocaine for post-operative analgesia in cats. Forty cats submitted to epidural administration of treatments followed by ovariohysterectomy were assessed in this study. The cats were randomly distributed into experimental groups: epidural control group (ECG), which received saline; epidural lidocaine group (ELG), which received 3 mg/kg of 2% lidocaine without vasoconstrictor; epidural maropitant group (EMG), which received 1 mg/kg of maropitant; and epidural lidocaine and maropitant group (ELM

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27

GOPURA, R. A. R. C., and Kazuo KIGUCHI. "1P1-E13 EMG-Based Control of a 6DOF Upper-Limb Exoskeleton Robot." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2009 (2009): _1P1—E13_1—_1P1—E13_3. http://dx.doi.org/10.1299/jsmermd.2009._1p1-e13_1.

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28

Yang, Geng Huang, Fei Fei Wang, Shi Gang Cui, Li Zhao, Qing Guo Meng, and Hong Da Chen. "Implementation of Human-Machine Interface Based on Electroencephalogram and Electromyography." Applied Mechanics and Materials 63-64 (June 2011): 385–89. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.385.

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The Electfoencephalogram (EEG) and electromyography (EMG) sampled from skin surface are the primary information to mirror the idea of human being. The human-machine interface based on EEG and EMG is used to control machine such as a robot. It is a new taste to apply this type of interface to some special condition such as an astronaut controlling the outside robot in a space ship. Digital signal processor (DSP) is used as sample EEG and EMG in the device. The feature of signal is extract by algorithm running in DSP to control the machine. The speech recognition based on fixed Chinese words is

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29

Drapała, Jarosław, Krzysztof Brzostowski, Agnieszka Szpala, and Alicja Rutkowska-Kucharska. "Two stage EMG onset detection method." Archives of Control Sciences 22, no. 4 (2012): 427–40. http://dx.doi.org/10.2478/v10170-011-0033-z.

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Detection of the moment when a muscle begins to activate on the basis of EMG signal is important task for a number of biomechanical studies. In order to provide high accuracy of EMG onset detection, we developed novel method, that give results similar to that obtained by an expert. By means of this method, EMG is processed in two stages. The first stage gives rough estimation of EMG onset, whereas the second stage performs local, precise searching. The method was applied to support signal processing in biomechanical study concerning effect of body position on EMG activity and peak muscle torqu

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30

Yeung, Jade, Peter G. R. Burke, Fiona L. Knapman, et al. "Task-dependent neural control of regions within human genioglossus." Journal of Applied Physiology 132, no. 2 (2022): 527–40. http://dx.doi.org/10.1152/japplphysiol.00478.2021.

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During swallowing, we observed two distinct, stereotyped muscle activation patterns that define the horizontal (monophasic, maximal EMG) and oblique (biphasic, submaximal EMG) neuromuscular compartments of genioglossus. In contrast, volitional tongue protrusions produced uniform activation across compartments. This provides evidence for task-dependent, functionally discrete neuromuscular control of the oblique and horizontal compartments of genioglossus. The magnitude and temporal patterning of genioglossus EMG during swallowing may help guide electrode placement in tongue EMG studies.

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31

Shakya, Sahaj, and Bipul Ranjitkar. "Forearm Bio-Medical Signal Processing." International Journal on Engineering Technology 2, no. 1 (2024): 49–59. https://doi.org/10.3126/injet.v2i1.72518.

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This research utilizes low-dimensional surface EMG and EEG data, obtained from the human arm using ECG electrodes, to analyze forearm muscle signals through a novel approach. Both EMG and EEG signals are employed side by side: EEG captures brain activity, particularly in the beta (13-30 Hz) and alpha (8-12 Hz) frequency ranges, while EMG focuses on muscle activity in the 20 Hz to 200 Hz range. Beta waves are associated with motor planning and voluntary movements, while alpha waves decrease during movement execution, indicating disengagement from a resting state. Event-related desynchronization

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32

Ting, Evon Lim Wan, Almon Chai, and Lim Phei Chin. "A Review on EMG Signal Classification and Applications." International Journal of Signal Processing Systems 9, no. 1 (2022): 1–6. http://dx.doi.org/10.18178/ijsps.10.1.1-6.

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Electromyography (EMG) signals are muscles signals that enable the identification of human movements without the need of complex human kinematics calculations. Researchers prefer EMG signals as input signals to control prosthetic arms and exoskeleton robots. However, the proper algorithm to classify human movements from raw EMG signals has been an interesting and challenging topic to researchers. Various studies have been carried out to produce EMG-based human movement classification that gives high accuracy and high reliability. In this paper, the methods used in EMG signal acquisition and pr

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33

Correia, Pedro, Carla Quintão, Cláudia Quaresma, and Ricardo Vigário. "A Framework for Corticomuscle Control Studies Using a Serious Gaming Approach." Methods and Protocols 8, no. 4 (2025): 74. https://doi.org/10.3390/mps8040074.

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Sophisticated voluntary movements are essential for everyday functioning, making the study of how the brain controls muscle activity a central challenge in neuroscience. Investigating corticomuscular control through non-invasive electrophysiological recordings is particularly complex due to the intricate nature of neuronal signals. To address this challenge, we present a novel experimental methodology designed to study corticomuscular control using electroencephalography (EEG) and electromyography (EMG). Our approach integrates a serious gaming biofeedback system with a specialized experimenta

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34

Chen, Minjie, and Honghai Liu. "Robot arm control method using forearm EMG signals." MATEC Web of Conferences 309 (2020): 04007. http://dx.doi.org/10.1051/matecconf/202030904007.

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With the continuous improvement of control technology and the continuous improvement of people’s living standards, the needs of disabled people for high-quality prosthetics have become increasingly strong. A control method of robotic arm based on surface electromyography signal (sEMG) of forearm is proposed. Firstly, the 16-channel EMG data of the forearm is obtained via the multi-channel EMG acquisition instrument and the electrode cuff as input signals, the features are extracted, then the gestures are classified and identified by the support-vector machine (SVM) algorithm, and the signals a

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35

Utkarsha, Wankhade* Dr. Shubhangi Giripunje. "DESIGN AND DEVELOPMENT OF EMG SIGNAL BASED SYSTEM FOR PARALYZED PEOPLE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 129–34. https://doi.org/10.5281/zenodo.496097.

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In recent years, physically disabled person faces more limitation in day to-day communication. There is a demand in today’s world for the development of a support system to physically disable people/paralyzed person. In this work, an EMG based control system for eye movement is proposed. Patients distressing from facial paralysis are on the risk of defacement and defeat of visualization due to failure of blink function. Eye movements tracking is helpful for disabled people suffering from Amyotrophic Lateral Sclerosis. In this research work, an EMG based eye control system for a paralyzed perso

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36

Zhang, Xu, Xiaoting Ren, Xiaoping Gao, Xiang Chen, and Ping Zhou. "Complexity Analysis of Surface EMG for Overcoming ECG Interference toward Proportional Myoelectric Control." Entropy 18, no. 4 (2016): 106. http://dx.doi.org/10.3390/e18040106.

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37

Jabbari, Milad, Rami Khushaba, and Kianoush Nazarpour. "Spatio-temporal warping for myoelectric control: an offline, feasibility study." Journal of Neural Engineering 18, no. 6 (2021): 066028. http://dx.doi.org/10.1088/1741-2552/ac387f.

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Abstract Objective. The efficacy of an adopted feature extraction method directly affects the classification of the electromyographic (EMG) signals in myoelectric control applications. Most methods attempt to extract the dynamics of the multi-channel EMG signals in the time domain and on a channel-by-channel, or at best pairs of channels, basis. However, considering multi-channel information to build a similarity matrix has not been taken into account. Approach. Combining methods of long and short-term memory (LSTM) and dynamic temporal warping, we developed a new feature, called spatio-tempor

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38

VeerSinghRana, Aditya, and Ridhi Aggarwal. "2-d Robotic Arm Control using EMG Signal." International Journal of Computer Applications 72, no. 14 (2013): 18–22. http://dx.doi.org/10.5120/12562-8944.

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39

Küçük, Serdar, and Umut Mayetin. "Wirless control of mobile robot with EMG signals." Pamukkale University Journal of Engineering Sciences 23, no. 5 (2017): 497–503. http://dx.doi.org/10.5505/pajes.2016.79735.

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40

Ide, Hideto, Ryosuke Hosaka, and Masao Ohtsuka. "Fuzzy Control of Robot Hand Based on EMG." Journal of Robotics and Mechatronics 3, no. 5 (1991): 435–36. http://dx.doi.org/10.20965/jrm.1991.p0435.

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41

Kumar,, Mr C. Sathish, Divya Sree H, Kumudhavalli S, Swathy G, and Thejaswini J. "Designing And Control of Prosthetic Leg Using EMG." International Journal of Research in Advent Technology 7, no. 3 (2019): 1503–4. http://dx.doi.org/10.32622/ijrat.732019136.

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42

Giuffrida, J. P., and P. E. Crago. "Reciprocal EMG control of elbow extension by FES." IEEE Transactions on Neural Systems and Rehabilitation Engineering 9, no. 4 (2001): 338–45. http://dx.doi.org/10.1109/7333.1000113.

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43

Reddy, Narender P., and Rajeev Unnikrishnan. "EMG Interfaces for VR and Telematic Control Applications." IFAC Proceedings Volumes 34, no. 9 (2001): 443–46. http://dx.doi.org/10.1016/s1474-6670(17)41746-0.

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44

Neilson, Peter D. "EMG bursts, sampling, and strategy in movement control." Behavioral and Brain Sciences 12, no. 2 (1989): 228–29. http://dx.doi.org/10.1017/s0140525x00048457.

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45

Bruun, E., and E. U. Haxthausen. "Current conveyor based EMG amplifier with shutdown control." Electronics Letters 27, no. 23 (1991): 2172. http://dx.doi.org/10.1049/el:19911344.

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46

El-Qadi, Ruaa, and Mohsin A. Al-Shammari. "EMG-Based Control of Active Ankle-Foot Prosthesis." Journal of Engineering 29, no. 09 (2023): 31–44. http://dx.doi.org/10.31026/j.eng.2023.09.03.

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Most below-knee prostheses are manufactured in Iraq without considering the fast progress in smart prostheses, which can offer movements in the desired directions according to the type of control system designed for this purpose. The proposed design appears to have the advantages of simplicity, affordability, better load distribution, suitability for subjects with transtibial amputation, and viability in countries with people having low socio-economic status. The designed prosthetics consisted of foot, ball, and socket joints, two stepper motors, a linkage system, and an EMG shield. All these

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47

Molano-Pulido, Renso Mardu, Félix Parca-Acevedo, Francia María Cabrera, and Henry Ñungo-Londoño. "Prototipo control de vehículo robot por señales EMG." Visión electrónica 15, no. 2 (2021): 264–71. http://dx.doi.org/10.14483/22484728.18948.

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Las señales EMG (Electromiografía) son básicamente pulsos eléctricos emitidos por los nervios y músculos de las extremidades del cuerpo humano, (ejemplo el bíceps del brazo) los que se obtienen por medio de electrodos. Estas señales se pueden amplificar y ser utilizadas en diferentes actividades o trabajos. En la presente investigación se utilizan las señales EMG, adquiridas del bíceps del brazo a utilizar, por medio de tres electrodos superficiales colocados específicamente para poder adquirir las señales trasmitidas por los músculos del bíceps, que con la utilización de un amplificador difer

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48

Aljobouri, Hadeel K. "A Virtual EMG Signal Control and Analysis for Optimal Hardware Design." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 02 (2022): 154–66. http://dx.doi.org/10.3991/ijoe.v18i02.27047.

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Background: A muscle-computer interface is one of the new applications of the human-computer interface technologies and specifically the brain-computer interface. Brain-muscle-computer interface based on the Electromyography (EMG) signal. EMG signal is an electrical activity from a muscle that is used as an input for effecting several tasks.Objective: This work presented an interfacing process between the Graphical User Interface (GUI) and hardware system. Using the implemented system, the researcher shall deals with the raw EMG data easily by analyzing the signal from the muscle sensor detect

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49

Wu, Changcheng, Aiguo Song, Yun Ling, Nan Wang, and Lei Tian. "A Control Strategy with Tactile Perception Feedback for EMG Prosthetic Hand." Journal of Sensors 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/869175.

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To improve the control effectiveness and make the prosthetic hand not only controllable but also perceivable, an EMG prosthetic hand control strategy was proposed in this paper. The control strategy consists of EMG self-learning motion recognition, backstepping controller with stiffness fuzzy observation, and force tactile representation. EMG self-learning motion recognition is used to reduce the influence on EMG signals caused by the uncertainty of the contacting position of the EMG sensors. Backstepping controller with stiffness fuzzy observation is used to realize the position control and g

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50

Ahn, Bummo, Seong Young Ko, and Gi-Hun Yang. "Compliance Control of Slave Manipulator Using EMG Signal for Telemanipulation." Applied Sciences 10, no. 4 (2020): 1431. http://dx.doi.org/10.3390/app10041431.

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Telemanipulation systems have been widely utilized in industrial, surgical, educational, and even military fields. One of the important issues is that when a robot interacts with environment or objects, it can damage the robot itself or the objects due to hard contact. To address this problem, we propose a novel compliance control of a slave robot using the electromyography (EMG) signal, which is measured by the sensors attached onto the master operator’s arm. The EMG signal is used since it is easy to process and it provides humans with an intuitive capability to perform the operational work.

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