Electromyography

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Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. (Wikipedia.org)


Conferences related to Electromyography

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2020 IEEE 16th International Workshop on Advanced Motion Control (AMC)

AMC2020 is the 16th in a series of biennial international workshops on Advanced Motion Control which aims to bring together researchers from both academia and industry and to promote omnipresent motion control technologies and applications.


2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII)

The world's premiere conference in MEMS sensors, actuators and integrated micro and nano systems welcomes you to attend this four-day event showcasing major technological, scientific and commercial breakthroughs in mechanical, optical, chemical and biological devices and systems using micro and nanotechnology.The major areas of activity in the development of Transducers solicited and expected at this conference include but are not limited to: Bio, Medical, Chemical, and Micro Total Analysis Systems Fabrication and Packaging Mechanical and Physical Sensors Materials and Characterization Design, Simulation and Theory Actuators Optical MEMS RF MEMS Nanotechnology Energy and Power


2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore


2019 IEEE 58th Conference on Decision and Control (CDC)

The CDC is recognized as the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, systems and control, and related areas.The 58th CDC will feature contributed and invited papers, as well as workshops and may include tutorial sessions.The IEEE CDC is hosted by the IEEE Control Systems Society (CSS) in cooperation with the Society for Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), the Japanese Society for Instrument and Control Engineers (SICE), and the European Union Control Association (EUCA).


2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC2019) will be held in the south of Europe in Bari, one of the most beautiful and historical cities in Italy. The Bari region’s nickname is “Little California” for its nice weather and Bari's cuisine is one of Italian most traditional , based of local seafood and olive oil. SMC2019 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report up-to-the-minute innovations and developments, summarize state­of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems and cybernetics. Advances have importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience, and thereby improve quality of life.


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Periodicals related to Electromyography

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Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Biomedical Engineering, IEEE Reviews in

The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Engineering in Medicine and Biology Magazine, IEEE

Both general and technical articles on current technologies and methods used in biomedical and clinical engineering; societal implications of medical technologies; current news items; book reviews; patent descriptions; and correspondence. Special interest departments, students, law, clinical engineering, ethics, new products, society news, historical features and government.


Geoscience and Remote Sensing, IEEE Transactions on

Theory, concepts, and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.


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Most published Xplore authors for Electromyography

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Xplore Articles related to Electromyography

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EMG Modeling and Simulation

[{u'author_order': 1, u'affiliation': u'Politecnico di Torino (Italy)', u'full_name': u'Roberto Merletti'}, {u'author_order': 2, u'full_name': u'Dario Farina'}] Surface Electromyography: Physiology, Engineering, and Applications, None

This chapter outlines the main components that should be considered in the development of physiologically based models of surface electromyographic (sEMG). One of the challenges in increasing the accuracy of current sEMG models is the correct identification of the model parameters. Parameter identification is particularly difficult for systems such as this in which it is not possible to accurately measure ...


Muscle Coordination, Motor Synergies, and Primitives from Surface EMG

[{u'author_order': 1, u'affiliation': u'Politecnico di Torino (Italy)', u'full_name': u'Roberto Merletti'}, {u'author_order': 2, u'full_name': u'Dario Farina'}] Surface Electromyography: Physiology, Engineering, and Applications, None

To investigate neural control strategies, muscle activity must be measured during motor behavior. Recent advances in the investigation of the neural control of movement have led to a re-examination of the mechanisms of sensorimotor integration in the central nervous system (CNS) and in the spinal circuitry in particular. This chapter considers different approaches used to uncover the modular organization of ...


Visual Acuity Improvement by Using a Biofeedback Based Adaptive Optics Controller

[] 2018 IEEE ANDESCON, 2018

Visual Acuity Improvement by Using a Biofeedback Based Adaptive Optics Controller


Electronic arms and legs: Meeting the bionic challenge: Though still far from creating a six-million-dollar man, the progress of electronic prosthesis technology is nothing short of a biotechnical wonder

[{u'author_order': 1, u'affiliation': u'Electronics technology department at Bramson ORT Technical Institute in New York City', u'full_name': u'Charles P. Rubenstein'}] IEEE Potentials, 1984

Progress in limb replacement, namely, the development of electronic prostheses, is described. The problems with closed-loop systems are discussed, and electromyographic knees are highlighted.


Surface Electromyography for MAN-Machine Interfacing in Rehabilitation Technologies

[{u'author_order': 1, u'affiliation': u'Politecnico di Torino (Italy)', u'full_name': u'Roberto Merletti'}, {u'author_order': 2, u'full_name': u'Dario Farina'}] Surface Electromyography: Physiology, Engineering, and Applications, None

This chapter describes the use of surface electromyography (EMG) for establishing intuitive man-machine interfaces for rehabilitation settings and technologies. The EMG is primarily used as source of neural information, to provide control signals for external devices. The chapter also describes the general concepts underlying the extraction of control signals from surface EMG recordings. It presents examples of neurorehabilitation technologies based ...


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Educational Resources on Electromyography

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IEEE-USA E-Books

  • EMG Modeling and Simulation

    This chapter outlines the main components that should be considered in the development of physiologically based models of surface electromyographic (sEMG). One of the challenges in increasing the accuracy of current sEMG models is the correct identification of the model parameters. Parameter identification is particularly difficult for systems such as this in which it is not possible to accurately measure all of the physiological, anatomical, and physical properties of the system. The chapter explores the simulation of intramuscular EMG, in particular for clinical applications. One of the primary purposes of a modeling approach is to identify the mechanisms responsible for experimental observations. A successful EMG model can thereby help the researcher to relate the recorded electrical signal to the underlying processes associated with muscle contraction. Model validation remains one of the most challenging areas in EMG modeling.

  • Muscle Coordination, Motor Synergies, and Primitives from Surface EMG

    To investigate neural control strategies, muscle activity must be measured during motor behavior. Recent advances in the investigation of the neural control of movement have led to a re-examination of the mechanisms of sensorimotor integration in the central nervous system (CNS) and in the spinal circuitry in particular. This chapter considers different approaches used to uncover the modular organization of the motor output in human behaviors such as responding to postural perturbations, reaching with the arm, and locomotion, as well as its plasticity and flexibility in movement disorders. It also investigates the strategies that the CNS employs to coordinate the activation of many muscles start from electromyographic (EMG) signals recorded simultaneously from many muscles. Different muscle synergies models are used to decompose the EMG envelopes using appropriate factorization algorithms. The chapter further considers the spatiotemporal organization of the activity patterns of leg and trunk muscles during locomotion.

  • Surface Electromyography for MAN-Machine Interfacing in Rehabilitation Technologies

    This chapter describes the use of surface electromyography (EMG) for establishing intuitive man-machine interfaces for rehabilitation settings and technologies. The EMG is primarily used as source of neural information, to provide control signals for external devices. The chapter also describes the general concepts underlying the extraction of control signals from surface EMG recordings. It presents examples of neurorehabilitation technologies based on surface EMG. The surface EMG signal can be used to trigger or continuously control external assistive devices including powered orthoses and prostheses. The chapter outlines representative examples of neurotechnologies that make use of EMG signals for device control purposes. It provides examples in the three main areas of neurorehabilitation: replacement, restoration, and neuromodulation. EMG signals were recorded from seven muscles including: vastus medialis, vastus lateralis, rectus femoris, lateral hamstrings, medial hamstrings, gastrocnemius medialis, and gastrocnemius lateralis.

  • Detection and Conditioning of Surface EMG Signals

    This chapter presents the detection and conditioning of surface electromyographic (EMG) signals. More advanced techniques are now widely used in research laboratories and are being adopted in clinical settings. Such techniques are based on multichannel detection by means of one dimensional (1-D) or two dimensional (2-D) electrode arrays. The chapter describes the electrode-skin interface and the front-end amplifier stage. The impedance between two electrodes is the sum of two electrode-skin impedances plus the interposed tissue impedance. Conventional electrodes, either wet or dry, behave like transducers converting ionic current (in tissue and gel) into flow of electrons in the metal. These electrical sensors require a careful skin preparation to reduce the impedance and noise associated to this interface. The biomedical sector offers small-sized, high-cost application-specific integrated circuits (ASIC) devices for biopotential measurements. Applications are expected to range from physiopathological investigations, to rehabilitation games, biofeedback applications, and sport training.

  • Techniques for Information Extraction from the Surface EMG Signalhigh-Density Surface EMG

    This chapter deals with the information that can be extracted from images obtained when electrode grids are applied to the skin above muscles with different architectures. The interpretation of these images, and therefore the information obtainable, depends on the muscle architecture and fiber arrangement. The action potentials propagating along muscle fibers generate electric fields in the surrounding conductive medium. The chapter deals with the surface EMG instantaneous images, with the feature images, and with the spatiotemporal images. It provides examples on the information that may be obtained from high-density surface EMG (HD-EMGs) detected from muscles with pinnate architecture. Two main fields of research application of HDsEMG may be identified such as surface EMG imaging and surface EMG decomposition. From the clinical point of view, the expected developments concern more extensive applications in biofeedback and monitoring of the neuromuscular system for prevention purposes and automatic detection of the innervation zone.

  • Surface EMG Decomposition

    This chapter provides an overview of surface EMG decomposition techniques, along with their basic assumptions, properties, and limitations. Surface electrodes measure the electrical activity of several nearby muscle fibers that are active during a muscle contraction. The electrical activity of each fiber can be described by a single fiber action potential (SFAP) that propagates from the neuromuscular junction towards the tendons. There is large diversity of decomposition techniques that can roughly be categorized either as template matching or latent component analysis (blind source separation) approaches. Decomposition of surface EMG is a powerful tool enabling noninvasive insight not only into muscle control strategies, but also into peripheral muscle properties. It provides unambiguous information on physiological parameters of individual motor units that can easily be interpreted. The identification of motor units (MUs) discharge patterns from surface EMG signals, acquired during dynamic muscle contractions, needs to be addressed.

  • Electromyography-Driven Modeling for Simulating Subject-Specific Movement at the Neuromusculoskeletal Level

    This chapter provides a comprehensive description of subject-specific electromyography (EMG)-driven musculoskeletal models for the human lower extremity. EMG-driven modeling requires experimental human motion data to be captured for model calibration and operation. A musculoskeletal model is created from medical imaging data of bone and muscle surfaces, such as magnetic resonance imaging (MRI) or computed tomography. The multi-degrees of freedom (DOFs) model comprises five main components: musculotendon kinematics, musculotendon activation, musculotendon dynamics, moment computation, and model calibration. The chapter demonstrates the use of EMG-driven modeling to predict musculotendon units (MTUs) forces and the resulting joint moments about multiple DOFs during dynamic motor tasks. It outlines the use of EMG- driven modeling for applications in neurorehabilitation technologies. EMG- driven methodologies can be successfully applied to study dynamic tasks that involve muscle co-contraction. EMG-informed predictions of muscle forces acting on the hip have been also used to improve estimates of bone remodeling stimulus.

  • Biophysics of the Generation of EMG Signals

    This chapter describes the basic concepts of generation and detection of EMG signals. Specific emphasis is devoted to the generation of muscle fiber action potentials at the fiber end plates, their propagation along the sarcolemma, and their extinction at the tendons. The chapter addresses the topics of crosstalk between nearby muscles and selectivity of the recording systems. It discusses the relationships between muscle force and the surface EMG. The EMG signal is generated by the electrical activity of the muscle fibers active during a contraction. The signal sources are the depolarizing and repolarizing zones of the muscle fibers. EMG signal features depend on a number of anatomical, physical, and detection system parameters. Considering all the factors related to the volume conductor and the signal sources that influence the characteristics of the EMG signal, a reliable relation between EMG amplitude and force needs a subject specific and condition specific calibration.

  • Single-Channel Techniques for Information Extraction from the Surface EMG Signal

    This chapter describes some of the most commonly used techniques for processing single-channel surface electromyographic (sEMG) signals. The single-channel techniques address the interference pattern that results from the simultaneous activation of many motor units (MUs). The chapter reviews spectral estimation and traditional stochastic models applicable to the electromyographic (EMG) signal. These models are used to develop, interpret, and test most of the signal processing techniques. EMG amplitude estimates have been assessed via a common application: the use of surface EMG to estimate joint torques. The chapter explores the basic body of knowledge concerning EMG spectral analysis and how physiological parameters are reflected by surface EMG power spectra. It further focuses on the theoretical basis for the assessment of muscle fatigue during high-intensity isometric constant force contractions since this assessment is by far the most prevalent application of surface EMG spectral analysis.

  • Conclusions and Future Work

    This chapter presents the conclusions described in the book related to the fusion of hard control strategies such as proportional integral‐derivative (PID), optimal, adaptive, and soft control strategies such as adaptive neuro‐fuzzy inference system (ANFIS), genetic algorithms (GA), particle swarm optimization (PSO), for a robotic or prosthetic hand. Chapter 2 of the book addressed the forward kinematics, inverse kinematics, and differential kinematics models of a serial n revolute‐joint planar two‐link thumb, and three‐link index finger. The fingertip (end‐effector) positions of each finger were derived by forward kinematics. Chapter 3 of the book described the dynamic equations of hand motion successfully derived via Lagrangian approach for two‐link thumb and three‐link fingers using the mathematical model of the actuator by using direct current (DC) motor and mechanical gears.


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