751 resources related to Neuromuscular stimulation
- Topics related to Neuromuscular stimulation
- IEEE Organizations related to Neuromuscular stimulation
- Conferences related to Neuromuscular stimulation
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2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting
The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science
The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
INTERMAG is the premier conference on all aspects of applied magnetism and provides a range of oral and poster presentations, invited talks and symposia, a tutorial session, and exhibits reviewing the latest developments in magnetism.
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 ...
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.
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.
Serves as a compendium for papers on the technological advances in control engineering and as an archival publication which will bridge the gap between theory and practice. Papers will highlight the latest knowledge, exploratory developments, and practical applications in all aspects of the technology needed to implement control systems from analysis and design through simulation and hardware.
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.
2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2006
Functional electrical stimulation (FES) is a method of restoring functional movements in patients of spinal cord injury that electrically induces muscle contractions. Nonlinear dynamics of paraplegics during standing up and limits of the physiological actuators suggest "nonlinear model predictive control" as a good candidate to follow desired trajectory and maintaining standing no need to learning or tuning many parameters such ...
IEEE Transactions on Biomedical Engineering, 2008
There are some essential problems with the arguments presented in the above paper about the design of a sliding-mode controller for functional electrical simulation (FES) induced control of knee-joint angle. In this note, we show that applying some approximations in derivation of the control law violates the reaching condition and could introduce some parasitic unmodeled dynamics in the sliding-mode control ...
1997 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM. 10 Years Networking the Pacific Rim, 1987-1997, 1997
To develop a powered hybrid FES (functional electrical stimulation) orthosis for paraplegics, fast system identification techniques play a crucial role to obtain information about the neuromuscular system. However, the underlying systems have nonlinear and coupled dynamics with uncertainties in practice. Artificial neural networks provide such adaptive nonlinear models and can be well extended to system identification or prediction problems. In ...
IEEE EMBS Asian-Pacific Conference on Biomedical Engineering, 2003., 2003
We have developed the multichannel closed loop FES controller using the PID control for multi-joint control and/or several degrees of freedom of movement control stimulating several muscles. Although the method was proved to be effective, it sometimes caused undesirable responses such as oscillations of joint angles because of strong nonlinearity of the input-output characteristics of electrically stimulated muscles. In this ...
IEEE Transactions on Rehabilitation Engineering, 2000
The knowledge of the behavior of electrically activated muscles is an important requisite for the development of functional electrical stimulation (FES) systems to restore mobility to persons with paralysis. The aim of this work was to develop a model capable of relating electrical parameters to dynamic joint torque for FES applications. The knee extensor muscles, stimulated using surface electrodes, were ...
Scientific Discovery & Deep Brain Stimulation: Jerrold Vitek, MD, PhD
Electrons May Be the New Pharmaceutical Drug
Transcranial Magnetic Stimulation with More Selective Targeting - IEEE Brain Workshop
From THz imaging to millimeter-wave stimulation of neurons: Is there a killer application for high frequency RF in the medical community? (RFIC 2015 Keynote)
Q&A with Kip Ludwig: IEEE Brain Podcast, Episode 7
Recent Advances in the Neural Dust Platform - IEEE Brain Workshop 2018
Ted Berger: Far Futures Panel - Technologies for Increasing Human Memory - TTM 2018
IEEE Brain: Nia Therapeutics: Building an Early-stage Medical Device Business
Q&A with Sri Sarma: IEEE Brain Podcast, Episode 2
Neuropriming: What Are The Ethical Implications? - IEEE TechEthics Virtual Panel
EMBC '09 - Technology's Role in Understanding and Treating Conditions of the Brain.
Wireless Framework Development for Personalized Rehabilitation - Angad Jasuja - IEEE EMBS at NIH, 2019
ICRA 2020 Keynote - Haptics for Humans in a Physically Distanced World
Prosthetic Hand Restores Amputee's Sense of Touch - IEEE Spectrum Report
IEEE Themes - Social dynamics in peer-to-peer sharing networks
Functional electrical stimulation (FES) is a method of restoring functional movements in patients of spinal cord injury that electrically induces muscle contractions. Nonlinear dynamics of paraplegics during standing up and limits of the physiological actuators suggest "nonlinear model predictive control" as a good candidate to follow desired trajectory and maintaining standing no need to learning or tuning many parameters such as many nonlinear controllers that had been used previously. In this work, the theoretical possibility of FES assisted standing up in paraplegic patient is studied using nonlinear model predictive control approach by computer simulation. The proposed controller shows good tracking behavior for ankle, knee and hip joint angles in simulated paraplegic standing up and improves patient safety because of considering constraints in control
There are some essential problems with the arguments presented in the above paper about the design of a sliding-mode controller for functional electrical simulation (FES) induced control of knee-joint angle. In this note, we show that applying some approximations in derivation of the control law violates the reaching condition and could introduce some parasitic unmodeled dynamics in the sliding-mode control loop. Therefore, the proposed controller cannot force the system into a sliding-mode regime, and its ability of producing a robust control loop with good tracking performance is theoretically under question.
To develop a powered hybrid FES (functional electrical stimulation) orthosis for paraplegics, fast system identification techniques play a crucial role to obtain information about the neuromuscular system. However, the underlying systems have nonlinear and coupled dynamics with uncertainties in practice. Artificial neural networks provide such adaptive nonlinear models and can be well extended to system identification or prediction problems. In this research, fast and robust online identification using a multi-layer recurrent neural network is tackled in two steps: a multi-layer network architecture through a supervisory-signal and error recurrent neural network (SERNN); and robust training through a modified recursive least-squares algorithm, having a dynamic forgetting factor and an optimal teaching signal for the hidden layer.
We have developed the multichannel closed loop FES controller using the PID control for multi-joint control and/or several degrees of freedom of movement control stimulating several muscles. Although the method was proved to be effective, it sometimes caused undesirable responses such as oscillations of joint angles because of strong nonlinearity of the input-output characteristics of electrically stimulated muscles. In this paper, a method of real-time parameter determination for the controller was examined. Controller parameters were determined based on stimulus intensities applied just before each adjustment of stimulation intensities. The method was effective in reducing undesirable oscillations during closed loop FES control without trial and error adjustment of controller parameters.
The knowledge of the behavior of electrically activated muscles is an important requisite for the development of functional electrical stimulation (FES) systems to restore mobility to persons with paralysis. The aim of this work was to develop a model capable of relating electrical parameters to dynamic joint torque for FES applications. The knee extensor muscles, stimulated using surface electrodes, were used for the experimental preparation. Both healthy subjects and people with paraplegia were tested. The dynamics of the lower limb were represented by a nonlinear second order model, which took account of the gravitational and inertial characteristics of the anatomical segments as well as the damping and stiffness properties of the knee joint. The viscous-elastic parameters of the system were identified experimentally through free pendular movements of the leg. Leg movements induced by quadriceps stimulation were acquired too, using a motion analysis system. Results showed that, for the considered experimental conditions, a simple one-pole transfer function is able to model the relationship between stimulus pulsewidth (PW) and active muscle torque. The time constant of the pole was found to depend on the stimulus pattern (ramp or step) while gain was directly dependent on stimulation frequency.
This study outlines the development of a mathematical model for predicting muscle force and motion in response to functional electrical stimulation. The mathematical model was developed by decomposing the muscle's contractile response into two distinct physiological steps: activation dynamics and the force dynamics, with the force dynamics being derived from the Hill-type model. By considering the activation dynamics, force-length and force-velocity relationships, and the influence of external loads, the model will predict the forces and movements due to external electrical stimulation of the muscle during non-isometric conditions.
Functional electrical stimulation of paralyzed hands can provide the user with some basic hand function. To improve the grasp provided by this method it is being attempted to use information from natural cutaneous sensors as recorded by a nerve cuff electrode implanted around a peripheral nerve. Slips across the skin can be detected in the nerve signal. This can be used to provide automatic intervention if an object being held starts to slip. A method for estimating the proper intensity of the reaction to a slip is presented. The method is based on the assumption that the reaction intensity should increase with the velocity of the slip and that the velocity is reflected in the amplitude of the nerve signal.
A 16-channel functional electrical stimulation (FES) system has been implanted in a person with T10 paraplegia for over a year. The system consists of two eight-channel radio frequency controlled receiver-stimulators delivering stimuli through a network of 14 epimysial and two intramuscular electrodes. Using this system and a walker for support, the subject was able to stand up for 8 min and walk regularly for 20 m. The standing duration was limited by arm fatigue since upper extremities supported an average of 25% of body weight. This was due to suboptimal hip extension and some undesired recruitment of rectus femoris and sartorius with stimulation of quadriceps electrodes. The left quadriceps exhibited rapid fatigue that limited walking distance and duration. The metabolic energy requirements were well within the aerobic limits of the sedentary paraplegic population. At one-year follow-up evaluation all electrodes are functional except one intramuscular electrode. The implant caused no adverse physiological effects and the individual reported health benefits such as increased energy and overall fitness as a result of the FES system use. With further improvements in muscle response through innovative surgical techniques, the 16-channel implanted FES system can be a viable addition to exercise and mobility function in persons with paraplegia.
The paper discusses an ART-1-based artificial neural network (ANY) adapted to controlling functional electrical stimulation (FES) to facilitate patient- responsive ambulation by paralyzed patients with spinal cord injuries. This network is designed to control FES systems developed by the first author and that is presently in use by over 400 patients worldwide (presently without ANN control) and which is the first and the only FES system approved by FDA. The network that is considered discriminates patterns of above-lesion upper-trunk electromyographic (EMG) time series to map patient's posture for activating standing and walking functions under FES and it controls FES stimuli levels using response-EMG signals to overcome muscle fatigue. The neural network also adaptively controls patient's postural stability via identifying changes in posture through acceleration/gravitational/weight sensors. The network trains itself to adapt to physiological changes of the patient, and it overcomes decision and control errors by simple punishment inputs from a single manual punishment switch. The system thus, is both self adaptive and patient- responsive through a combinations of neural EMG signals and an artificial neural network to achieve patient responsive ambulation using stimulation of the patient's own peripheral motor neurons, namely his own peripheral neural network.
A method for predicting shoulder and motions from electromyograms (EMGs) from shoulder muscles using a time-delayed artificial neural network (TDANN) is described. The chosen network was found to be capable of characterizing the nonlinear and dynamic relationship between the EMG signals recorded from 6 shoulder muscles and the resulting shoulder and elbow motions in 5 able-bodied subjects. Preliminary work in one individual with tetraplegia due to spinal cord injury indicate that the same TDANN structure (although with a different set of muscle EMGs) will be also be sufficient to detect these motions in this population. This ability to detect shoulder and elbow motions would allow neuroprostheses based on functional neuromuscular stimulation (FNS) to appropriately vary stimulation patterns in a very natural manner for different tasks.
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