Relevant bibliographies by topics / EMG control

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'EMG control'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "EMG control"

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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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Awari, Rohan, Prof R. B. Kakkeri, and Radha Chande. "EMG Based Robot Control." IJIREEICE 7, no. 5 (May 30, 2019): 14–16. http://dx.doi.org/10.17148/ijireeice.2019.7504.

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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 (December 10, 2018): 35–40. http://dx.doi.org/10.5185/amlett.2019.2171.

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

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Gordleeva, S. Yu, S. A. Lobov, V. I. Mironov, I. A. Kastalskiy, M. V. Lukoyanov, N. P. Krilova, I. V. Mukhina, A. Ya Kaplan, and V. B. Kazantsev. "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 (September 15, 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 of classified EEG- and EMG-patterns and muscle effort estimation. The number of controlled devices includes mobile robot LEGO NXT Mindstorms (LEGO, Denmark), humanoid robot NAO (Aldebaran, France) and exoskeleton Ilia Muromets (UNN, Russia). Results. We have developed and tested an interface combining command and proportional control based on EMG signals. We have determined the parameters providing optimal characteristics of classification accuracy of EMG patterns, as well as the speed and accuracy of proportional control. Also we have developed and tested a BCI interface based on motor imagined patterns. Both EMG and EEG interfaces are included into hardware and software system. The system combines outputs of the interfaces and sends commands to a robotic device. Conclusion. We have developed and approved the hardware-software system on the basis of the combined command-proportional EMG and EEG control of external robotic devices.
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Danna-Dos-Santos, Alessander, Tjeerd W. Boonstra, Adriana M. Degani, Vinicius S. Cardoso, Alessandra T. Magalhaes, Luis Mochizuki, and Charles T. Leonard. "Multi-muscle control during bipedal stance: an EMG–EMG analysis approach." Experimental Brain Research 232, no. 1 (October 9, 2013): 75–87. http://dx.doi.org/10.1007/s00221-013-3721-z.

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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 (July 2006): 503–14. http://dx.doi.org/10.1109/tsmcc.2006.875418.

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

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

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Dissertations / Theses on the topic "EMG control"

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Boyd, William J. "EMG Site: A MATLAB-based Application for EMG Data Collection and EMG-based Prosthetic Control." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/351.

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This thesis describes the system design of EMG Site, a MATLAB-based application for collection and visualization of surface electromyograms (EMGs) and the real-time control of an upper limb prosthesis, including details pertaining to the design of the software and the graphical user interface (GUI). The application consists of features that aid in the visualization of the collected EMG data and the control of a prosthesis. Visualization of the collected EMG data is handled in one of two ways: an oscilloscope-like view showing the raw EMG data collected with respect to time, or a radial plot showing the processed EMG data collected with respect to the site of EMG data collection on the arm. The control of a hand-wrist prosthesis is primarily regulated through the use of signal processing designed to relate EMG to torque and is visualized in the tracking window - a plotting window showing both a user-control cursor and an either static (or dynamic) computer-controlled target. This thesis concludes with a description of the real-time capabilities of the application regarding both the visualization of the collected EMG data as well as the control of a prosthesis.
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Wang, Jing M. Eng Massachusetts Institute of Technology. "EMG control of prosthetic ankle plantar flexion." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/76110.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 59-60).
Similar to biological human ankle, today's commercially available powered ankle-foot prostheses can vary impedance and deliver net positive ankle work. These commercially available prostheses are intrinsically controlled. Users cannot intuitively change ankle controller's behavior to perform movements that are not part of the repetitive walking gait cycle. For example, when transition from level ground walking to descending stairs, user cannot intuitively initiate or control the amount of ankle angle deflexion for a more normative stair descent gait pattern. This paper presents a hybrid controller that adds myoelectric control functionality to an existing intrinsic controller. The system employs input from both mechanical sensors on the ankle as well as myoelectric signals from gastrocnemius muscle of the user. This control scheme lets the user to modulate the gain of command ankle torque upon push off during level ground walking and stair ascent. It also allows the user to interrupt level ground walking control cycle and initiate ankle plantar flexion during stair descent. As a preliminary study, ankle characteristics such as ankle angle and torque were measured and compared to biological ankle characteristics. Results show that the proposed hybrid controller can maintain existing controller's biomimetic characteristics. In addition, it can also recognize to a qualitative extent the intended command torque for ankle push off and user's desire to switch between control modalities for different terrains. The study shows that it is possible and desirable to use neural signals as control signals for prosthetic leg controllers. Keyword: Myoelectric control, powered prosthesis, proportional torque control
by Jing Wang.
M.Eng.
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Baccherini, Simona. "Pattern recognition methods for EMG prosthetic control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12033/.

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In this work we focus on pattern recognition methods related to EMG upper-limb prosthetic control. After giving a detailed review of the most widely used classification methods, we propose a new classification approach. It comes as a result of comparison in the Fourier analysis between able-bodied and trans-radial amputee subjects. We thus suggest a different classification method which considers each surface electrodes contribute separately, together with five time domain features, obtaining an average classification accuracy equals to 75% on a sample of trans-radial amputees. We propose an automatic feature selection procedure as a minimization problem in order to improve the method and its robustness.
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Peña, Guido Gómez. "Controle de impedância adaptativo dirigido por EMG para reabilitacão robótica." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-19032019-144320/.

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Esta tese trata da estimativa de torque e rigidez do paciente dirigida por EMG e sua utilização para adaptar a rigidez do robô durante a reabilitação assistida por robôs. Os sinais eletromiográficos (EMG), obtidos de músculos que atuam durante os movimentos de flexão e extensão de um usuário utilizando uma órtese de joelho ativa, são processados para obter as ativações dos músculos. Inicialmente, um modelo musculoesquelético simplificado e otimizado é usado para calcular as estimativas de torque e rigidez da junta do paciente. A otimização do modelo é realizada comparando o torque estimado com o torque gerado pela ferramenta de dinâmica inversa do software OpenSim, considerando um modelo musculoesquelético escalonado. Como uma solução complementar, é proposta uma rede neural perceptron multicamada (NN) para mapear os sinais EMG para o torque do paciente. Também é apresentado um Ambiente de Estimativa de Torque Gerado por EMG criado para analisar os dados obtidos a partir da aplicação das abordagens propostas considerando a aplicação de um protocolo criado para a análise de interação usuário-exoesqueleto. Um banco de dados de indivíduos saudáveis também foi disponibilizado neste trabalho. Além disso, uma estratégia de controle de impedância adaptativa é proposta para ajustar a rigidez do robô com base na estimativa de rigidez do paciente por EMG. A estratégia inclui uma solução ideal para a interação paciente-robô. Finalmente, são apresentados os resultados obtidos aplicando o controle de impedância adaptativo proposto durante os movimentos de flexão e extensão do usuário que usa a órtese ativa.
This thesis deals with EMG-driven patient torque and stiffness estimation and its use to adapt the robot stiffness during robot-aided rehabilitation. Electromyographic (EMG) signals, taken from selected muscles acting during flexion and extension movements of an user wearing an active knee orthosis, are processed to get the muscles activations. First, a simplified and optimized musculoskeletal model is used to compute the estimate of patient joint torque and stiffness. The model optimization is performed by comparing the estimate torque with the torque generated by the inverse dynamics tool of the OpenSim software, considering a scaled musculoskeletal model. As a complementary solution, a multilayer perceptron neural network (NN) is proposed to map the EMG signals to the patient torque. It is also presented an EMG-driven Torque Estimation Environment created to analyze the data obtained from the application of the proposed approaches considering a protocol created for user-exoskeleton interaction analysis. A database with data from 5 healthy subjects is also made available in this work. Additionally, an adaptive impedance control strategy is proposed to adjust the robot stiffness based on the EMG-driven patient stiffness estimation. The strategy includes an optimal solution for the patient-robot interaction. Finally, the results obtained by applying the proposed adaptive impedance control during flexion and extension movements of the user wearing the active orthosis are presented.
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Grisetto, Fanny. "Impulsivity is not just disinhibition : investigating the effects of impulsivity on the adaptation of cognitive control mechanisms." Thesis, Lille 3, 2020. http://www.theses.fr/2020LIL3H031.

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L’impulsivité est une tendance comportementale fréquemment observée dans la population générale mais à des degrés différents. À ce propos, une forte impulsivité augmente les risques de développer un trouble psychiatrique, tel que les différentes formes d’addiction ou des troubles de la personnalité. Pour comprendre l’émergence de ces divers troubles comportementaux, mon projet de thèse s’est porté sur le rôle du contrôle cognitif dans les manifestations de l’impulsivité. Le contrôle cognitif est, en effet, un ensemble de fonctions cognitives nous permettant d’adapter nos comportements à un environnement changeant, et donc complexe. Durant ma thèse, je me suis plus particulièrement intéressée aux capacités d’adaptation des mécanismes de contrôle proactif et réactif chez des individus impulsifs, principalement dans la population générale mais également auprès de patients alcoolo-dépendants.Les trois premières études de ma thèse ont montré qu’une forte impulsivité était caractérisée par une utilisation moindre des mécanismes proactifs associée à un défaut d’adaptation des mécanismes de contrôle aux demandes externes et aux contraintes internes.Les individus impulsifs exercent moins de contrôle proactif alors que celui-ci devrait être favorisé au vu des caractéristiques contextuelles ou individuelles. Dans une quatrième étude dans laquelle des enregistrements EEG ont été effectués, nous nous sommes intéressées à l’activité cérébrale typique observée au moment de l’exécution des erreurs,nommée ERN/Ne, et dont le rôle serait de signaler les besoins en contrôle. Une réduction de cette activité cérébrale a été observée chez les individus les plus agressifs, mais pas chez les individus les plus impulsifs. Ce résultat suggère que l’émergence de comportements inadaptés pourrait être en partie expliquée par cette réduction du signal d’alarme. Enfin,des résultats préliminaires suggèrent un lien entre un indice périphérique de l’adaptation physiologique (HRV) et les capacités d’adaptation des mécanismes de contrôle. Ce résultat ouvre la voie à de nouvelles interventions thérapeutiques pour la réduction des comportements inadaptés.Dans l’ensemble, les résultats de cette thèse suggèrent que l’impulsivité en population générale est associée à un système de contrôle cognitif moins proactif et moins flexible,menant potentiellement à des comportements inappropriés quand les mécanismes de contrôle en jeu sont inadaptés
Impulsivity is a behavioral tendency frequently observed in the general population butat different degrees. Interestingly, higher impulsivity increases the probability to develop and to be diagnosed with a psychiatric disorder, such as substance use or personality disorders. To gain a better understanding on the emergence of such psychiatric disorders, my PhD project focused on the role of cognitive control in impulsive manifestations. Indeed, cognitive control is a set of basic executive functions ensuring adaptive behaviors to an ever-changing and complex environment. More particularly, during my PhD research, I investigated the flexible adaptation between reactive and proactive control mechanisms in impulsive individuals, mainly from the general population but also from an alcohol-dependent population.The first three studies of my thesis revealed that high impulsivity was characterizedby a less-proactive cognitive control system, and associated with a weaker adaptation ofcognitive control mechanisms both to external demands and internal constraints. Morespecifically, I observed that high impulsive individuals less exert proactive control whileit should be favored given contextual or individual characteristics. In the fourth study inwhich EEG signals were recorded, we were interested in the brain activity that is typicallyobserved during errors (i.e., the ERN/Ne), which is thought to signal the need for control.A reduction in this brain activity was observed in high aggressive individuals, but notin high impulsive individuals. This finding suggest that the emergence of maladaptivebehaviors may be explained, to a certain extent, by the reduced alarm signal. Finally, somepreliminary results suggest a link between a peripheral index of physiological adaptation(i.e., HRV) and the capacity to adapt control mechanisms. These findings open newavenues for therapeutic interventions in the reduction in maladaptive behaviors.Overall, findings from the current thesis suggest that impulsivity in the general population is associated with a less proactive and a less flexible cognitive control system, potentially leading to inappropriate behaviors when the control mechanisms at play are maladapted
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Ammendrup, Katrin. "Framework for Wireless Acquisition of Surface EMG and Real-Time Control." Thesis, KTH, Medicinteknik och hälsosystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233311.

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Muscle driven devices are controlled or powered with muscle activation. These devices open up the possibility of offering patients with limited muscle function to automatically control assistive devices - for instance exoskeletons - with input from their own muscles. This solution would help a number of patient groups suffering from common conditions, such as spinal cord injuries, stroke and cerebral palsy. To use muscle activation as input it is necessary to have a way to communicate with the mus- cles. Electromyography (EMG) is a technology used to gain information about muscle function and activation. It is performed by measuring and analyzing electrical signals conducted by the muscles during activation. Activation and activation level can be seen from analyzed EMG signal. EMG signals are frequently measured and analyzed afterwards, however, to use it as a controlling an assistive devices, real time analysis is necessary. In this thesis real time acquisi- tion and analysis of EMG was performed. The measured signal was used as an input to control a simple MATLAB computer game. The EMG of a muscle of the forearm, Brachioradialis, was measured with Myon Aktos sys- tem. The measured signal was written to a server as soon as the measurements were acquired. MATLAB was used to connect to the server and performing the signal analysis. The analysis was kept simple in order to limit delay. The result showed that it was possible to acquire real time signal with this method. The delay was negligible, both for the testing and for the game play. Showing that it is possible to play a game with muscle activation supports the idea of a motor that can be controlled automatically with muscle input. Future work should focus on understanding movement intent with respect to EMG and on analyzing multiple signals from different muscles at the same time.
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Laine, Christopher. "Decoding the Language of Hypoglossal Motor Control." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/203440.

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To effect movement, the central nervous system must appropriately coordinate the activities of pools of motoneurons (MNs), the cells which control muscle fibers. Sources of neural drive are often distributed to many MNs of a pool, and thus can synchronize the activities of targeted MNs. In this thesis, synchronization among MNs is used to investigate the strength, temporal progression, and anatomical distribution of neural drive to the hypoglossal motor nucleus (HMN), which controls muscles of the tongue. The HMN is an ideal target for such an investigation because it processes a host of functionally diverse inputs, such as those related to breathing, speaking, and swallowing. Study 1 characterizes motor unit (MU) synchronization within and across bellies of the human genioglossus (GG) muscle when MUs are activated by cortical drive (during voluntary tongue protrusion) or by automatic, brainstem-mediated drive (during rest breathing). We show that voluntary tongue protrusion synchronizes MU spike timing and firing rates within but not across bellies of the GG, whereas during rest breathing, MU firing rates are moderately synchronized both within and across muscle bellies. Study 2 documents respiratory-related synchronization of MU activities in muscles of the tongue and respiratory pump using an anesthetized rat model. The results of this study indicate that upper airway and respiratory pump MN pools share a low frequency respiratory-related drive, but that higher frequency (>8 Hz) synchronization is strongest in MU pairs of the chest-wall. Finally, Study 3 examines the potential for GG multi-unit and single MU activities to be entrained by cortical input. We show that during voluntary tongue protrusion, cortical oscillations in the 15-40 Hz range weakly synchronize MU population activity, and that EEG oscillations in this range intermittently influence the spike timing of individual GG MUs. These studies are the first to characterize MU synchronization by different sources of neural input to the HMN and establish a broad foundation for further investigation of hypoglossal motor control.
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Tarullo, Viviana. "Artificial Neural Networks for classification of EMG data in hand myoelectric control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19195/.

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This thesis studies the state-of-the-art in myoelectric control of active hand prostheses for people with trans-radial amputation using pattern recognition and machine learning techniques. Our work is supported by Centro Protesi INAIL in Vigorso di Budrio (BO). We studied the control system developed by INAIL consisting in acquiring EMG signals from amputee subjects and using pattern recognition methods for the classifcation of acquired signals, associating them with specifc gestures and consequently commanding the prosthesis. Our work consisted in improving classifcation methods used in the learning phase. In particular, we proposed a classifer based on a neural network as a valid alternative to the INAIL one-versus-all approach to multiclass classifcation.
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Simkin, Laurey R. "The effects of performance feedback and EMG biofeedback contingency on self-perceptions." Scholarly Commons, 1986. https://scholarlycommons.pacific.edu/uop_etds/2123.

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Källman, Alexandra, and Nina Nylander. "Läppasymmetrier hos stammande och icke-stammande personer : En EMG-studie." Thesis, Uppsala universitet, Logopedi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242637.

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Det har tidigare rapporterats att icke-stammande personer har en tydlig vänstersidig cerebral lateralisering vad gäller talmotoriken, vilket visas genom ökad högersidig aktivering i läppmuskulaturen. Personer med stamning har å andra sidan föreslagits ha ett vänstersidigt eller bilateralt aktiveringsmönster av talmuskulaturen. Detta antas vara en av orsakerna till stamning. Testdeltagarna i aktuell studie bestod av 11 stammande och 13 matchade icke-stammande vuxna. Uppgifterna bestod av ordrepetition, ordgenerering samt att puta med läpparna. EMG-aktivitet från musklerna orbicularis oris och depressor labii inferior registrerades med elektroder på höger och vänster sida av läppmuskulaturen. Syftet med aktuell studie var att undersöka eventuell skillnad i läppasymmetri mellan stammande och icke-stammande vuxna personer. Resultatet kunde inte påvisa signifikanta gruppskillnader i grad av muskelaktivering för läppmuskulaturen. Båda grupperna uppvisade tvärtemot förväntan en nästintill bilateral aktivitet. En tendens till positiv korrelation, om än inte statistiskt signifikant, kunde observeras mellan grad av uppvisad stamning och något mer högersidig muskelaktivering.
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Books on the topic "EMG control"

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Chau, Tom. Pattern recognition of processed EMG signals for two-site myoelectric control. Ottawa: National Library of Canada, 1994.

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White, Donald R. J. EMI control methodology and procedures. Gainesville, Va: Interference Control Technologies, 1988.

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White, Donald R. J. EMI control methodology and procedures. 4th ed. Gainesville, Va. (P.O. Box D, Gainesville 22065): Interference Control Technologies, 1985.

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Archambeault, Bruce R. PCB Design for Real-World EMI Control. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3640-3.

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Archambeault, Bruce. PCB design for real-world EMI control. Boston: Kluwer Academic Publishers, 2002.

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Fund, International Monetary. Credibility, capital controls, and the EMS. Washington, D. C: International Monetary Fund, 1989.

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Sithanantham, S., Chandish R. Ballal, S. K. Jalali, and N. Bakthavatsalam, eds. Biological Control of Insect Pests Using Egg Parasitoids. New Delhi: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1181-5.

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Dale, Burg, ed. Reclaim your nest egg: Take control of your financial future. New York: Bloomberg Press, 2010.

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Cônsoli, Fernando L. Egg Parasitoids in Agroecosystems with Emphasis on Trichogramma. Dordrecht: Springer Science+Business Media B.V., 2010.

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Nelms, R. M. Design of power electronics for TVC & EMA systems: Final report. [Washington, DC: National Aeronautics and Space Administration, 1994.

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Book chapters on the topic "EMG control"

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Santana, Aashish, and Chenguang Yang. "Robotic Control Using Physiological EMG and EEG Signals." In Advances in Autonomous Robotics, 449–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32527-4_53.

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Korczyński, R., S. Kasicki, and U. Borecka. "EMG and Hippocampal EEG Activities during Spontaneous and Elicited Movements in the Rat." In Motor Control, 75–78. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4615-7508-5_13.

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Li, Guanglin, Oluwarotimi Williams Samuel, Chuang Lin, Mojisola Grace Asogbon, Peng Fang, and Paul Oluwagbengba Idowu. "Realizing Efficient EMG-Based Prosthetic Control Strategy." In Advances in Experimental Medicine and Biology, 149–66. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2050-7_6.

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Nunsanga, Morrel V. L., Y. Thanrun Kumar, Bikrant Kumar, Murad Alam Mirja, and Rajesh Kumar. "IoT Based Control of Robotic Arm Using EMG Signals." In Learning and Analytics in Intelligent Systems, 1013–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42363-6_117.

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Dupan, Sigrid S. G., Ivan Vujaklija, Giulia De Vitis, Strahinja S. Dosen, Dario Farina, and Dick F. Stegeman. "HD-EMG to Assess Motor Learning in Myoelectric Control." In Converging Clinical and Engineering Research on Neurorehabilitation III, 1131–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01845-0_226.

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Bollens, E., and J. P. Clarys. "Peripheral EMG Control of Handpaddle Influence on Swimming Movements." In Biomechanics: Current Interdisciplinary Research, 699–704. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-011-7432-9_106.

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Wolczowski, Andrzej, and Marek Kurzynski. "Control of Artificial Hand via Recognition of EMG Signals." In Biological and Medical Data Analysis, 356–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30547-7_36.

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Wang, Nianfeng, Kunyi Lao, and Xianmin Zhang. "Design of an Anthropomorphic Prosthetic Hand with EMG Control." In Intelligent Robotics and Applications, 300–308. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13966-1_30.

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Huynh, Khanh Quoc, Nga Thi-Hang Vu, Nam Hoang Bui, and Hien Thi-Thu Pham. "Building an EMG Receiver System to Control a Peripheral Device." In IFMBE Proceedings, 61–66. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5859-3_11.

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Suberbiola, Aaron, Ekaitz Zulueta, Jose Manuel Lopez-Guede, Ismael Etxeberria-Agiriano, and Bren Van Caesbroeck. "Arm Orthosis/Prosthesis Control Based on Surface EMG Signal Extraction." In Lecture Notes in Computer Science, 510–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40846-5_51.

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Conference papers on the topic "EMG control"

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Ura, Kazuhide, Teruyoshi Sadahiro, Masami Iwase, and Shoshiro Hatakeyama. "Zero-phase tracking human interface using EMG signals and EMD." In Control (MSC). IEEE, 2011. http://dx.doi.org/10.1109/cca.2011.6044351.

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Sa-e, Sakariya, Chris T. Freeman, and Kai Yang. "Model-Based Control of FES Embedding Simultaneous Volitional EMG Measurement." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516718.

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Wang, Leilei, Shuo Du, Huan Liu, Jinxu Yu, Shengcui Cheng, and Ping Xie. "A virtual rehabilitation system based on EEG-EMG feedback control." In 2017 Chinese Automation Congress (CAC). IEEE, 2017. http://dx.doi.org/10.1109/cac.2017.8243542.

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de Oliveira, Juliette de Paula Felipe, Ernano Arrais, and Valentin Obac Roda. "A reconfigurable control system using EMG." In 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2014. http://dx.doi.org/10.1109/i2mtc.2014.6860959.

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Wong, Farrah, Sia Chiew Lian, Chan Bun Seng, Lim Pei Yi, Renee Chin, Kenneth Teo, Ali Chekima, and Khairul Anuar Mohamad. "Model cart control using EMG signal." In 2015 10th Asian Control Conference (ASCC). IEEE, 2015. http://dx.doi.org/10.1109/ascc.2015.7244693.

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Ali, Maham, Areeba Riaz, Waleef Ullah Usmani, and Noman Naseer. "EMG Based Control of a Quadcopter." In 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT). IEEE, 2020. http://dx.doi.org/10.1109/mecnit48290.2020.9166603.

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Patel, Aditya, James Ramsay, Mohammad Imtiaz, and Yufeng Lu. "EMG-based Human Machine Interface Control." In 2019 12th International Conference on Human System Interaction (HSI). IEEE, 2019. http://dx.doi.org/10.1109/hsi47298.2019.8942598.

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Anis, Anoosha, M. Abbas Irshad, Syed M. Hamza, Noman Naseer, Hammad Nazeer, and Andrian. "EMG based Control of Transtibial Prosthesis." In International Conference on Health Informatics and Medical Application Technology. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0009464200740081.

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Khan, Sagheer, Kiran Khurshid, and Muhammad Zceshan. "EMG Data Acquisition and Flight Control of Quadcopter on Different EMG Signals." In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2019. http://dx.doi.org/10.23919/cisti.2019.8760908.

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Nawrocka, Agata, Marcin Nawrocki, and Andrzej Kot. "Analysis of the Biological (EMG) Signal." In 2020 21th International Carpathian Control Conference (ICCC). IEEE, 2020. http://dx.doi.org/10.1109/iccc49264.2020.9257274.

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Reports on the topic "EMG control"

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Smith, J. R., and R. Gough. Electromechanical Battery (EMB) and EMB Power Control System Final Report CRADA No. TC-723-94. Office of Scientific and Technical Information (OSTI), February 1996. http://dx.doi.org/10.2172/1438807.

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Brunner, Amy, and Jason Holliday. Abiotic stress networks converging on FT2 to control growth in Populus. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1484373.

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Romberger, J. Chapter 19: HVAC Controls (DDC/EMS/BAS) Evaluation Protocol. Office of Scientific and Technical Information (OSTI), November 2014. http://dx.doi.org/10.2172/1164874.

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Zhu, Yijun. Identification of Small Molecules Targeting the Posttranscriptional Control of ERG Expression. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada575222.

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Reaves, Jimmy L., and Ralph H. Crawford. In vitro colony interactions among species of Trichoderma with inference toward biological control. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 1994. http://dx.doi.org/10.2737/pnw-rp-474.

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Capela dos Santos, Denise. A política de controlo de doenças transmissíveis em Portugal. Universidade Autónoma de Lisboa, 2016. http://dx.doi.org/10.26619/ual-cee/wp012016.

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Steigerwalt, Ryan. Quality Control Methodologies for Advanced EMI Sensor Data Acquisition and Anomaly Classification - Former Southwestern Proving Ground, Arkansas. Fort Belvoir, VA: Defense Technical Information Center, July 2015. http://dx.doi.org/10.21236/ada626409.

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Solomon, J. D. Early Impact and Control of Aphid (Chaitophorus populicola Thomas) Infestations on Young Cottonwood Plantations in the Mississippi Delta. New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, 1999. http://dx.doi.org/10.2737/so-rn-326.

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Werley, Kenneth Alan, and Andrew William Mccown. Interface Control Document for the EMPACT Module that Estimates Electric Power Transmission System Response to EMP-Caused Damage. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1259633.

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Cruz, Tássia, David Plank, Gregory Elacqua, Luana Marotta, Sammara Soares, and João Cossi. Novo Fundeb: Prós e contras das propostas em debate. Inter-American Development Bank, September 2019. http://dx.doi.org/10.18235/0001853.

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