Relevant bibliographies by topics / Myoelectric signal

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

Academic literature on the topic 'Myoelectric signal'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Myoelectric signal"

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Scott, R. N., D. MacIsaac, and P. A. Parker. "Non-stationary Myoelectric Signals and Muscle Fatigue." Methods of Information in Medicine 39, no. 02 (2000): 125–29. http://dx.doi.org/10.1055/s-0038-1634281.

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Abstract:A mathematical derivation for the mean frequency of a myoelectric signal (MES) is provided based on an amplitude modulation model for non-stationary MES. With this derivation, it is shown that mean frequency estimates of stationary and non-stationary myoelectric signals theoretically are not significantly different in a physiologically practical context. While this prediction is confirmed via a computer simulation, it is refuted with empirical evidence. Regardless, it is shown in a final study that mean frequency is capable of tracking a downward shift in the power spectrum with fatigue even in non-stationary myoelectric signals.
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Yoo, Hyun-Joon, Hyeong-jun Park, and Boreom Lee. "Myoelectric Signal Classification of Targeted Muscles Using Dictionary Learning." Sensors 19, no. 10 (2019): 2370. http://dx.doi.org/10.3390/s19102370.

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Surface electromyography (sEMG) signals comprise electrophysiological information related to muscle activity. As this signal is easy to record, it is utilized to control several myoelectric prostheses devices. Several studies have been conducted to process sEMG signals more efficiently. However, research on optimal algorithms and electrode placements for the processing of sEMG signals is still inconclusive. In addition, very few studies have focused on minimizing the number of electrodes. In this study, we investigated the most effective method for myoelectric signal classification with a small number of electrodes. A total of 23 subjects participated in the study, and the sEMG data of 14 different hand movements of the subjects were acquired from targeted muscles and untargeted muscles. Furthermore, the study compared the classification accuracy of the sEMG data using discriminative feature-oriented dictionary learning (DFDL) and other conventional classifiers. DFDL demonstrated the highest classification accuracy among the classifiers, and its higher quality performance became more apparent as the number of channels decreased. The targeted method was superior to the untargeted method, particularly when classifying sEMG signals with DFDL. Therefore, it was concluded that the combination of the targeted method and the DFDL algorithm could classify myoelectric signals more effectively with a minimal number of channels.
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Kelly, M. F., P. A. Parker, and R. N. Scott. "Myoelectric signal analysis using neural networks." IEEE Engineering in Medicine and Biology Magazine 9, no. 1 (1990): 61–64. http://dx.doi.org/10.1109/51.62909.

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ONUKI, Tomoya, and Nobuhiko HENMI. "1105 Engineering design using myoelectric Signal." Proceedings of Conference of Hokuriku-Shinetsu Branch 2012.49 (2012): 110501–2. http://dx.doi.org/10.1299/jsmehs.2012.49.110501.

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Knaflitz, M., and G. Balestra. "Computer analysis of the myoelectric signal." IEEE Micro 11, no. 5 (1991): 12–15. http://dx.doi.org/10.1109/40.108544.

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Ungureanu, Mihaela, Rodica Strungaru, and Vasile Lazarescu. "Myoelectric Signal Classification Using Neural Networks." Biomedizinische Technik/Biomedical Engineering 43, s3 (1998): 87–90. http://dx.doi.org/10.1515/bmte.1998.43.s3.87.

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Basha, T., R. N. Scott, P. A. Parker, and B. S. Hudgins. "Deterministic components in the myoelectric signal." Medical & Biological Engineering & Computing 32, no. 2 (1994): 233–35. http://dx.doi.org/10.1007/bf02518927.

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De Luca, Carlo J., Mohamed A. Sabbahi, and Serge H. Roy. "Median frequency of the myoelectric signal." European Journal of Applied Physiology and Occupational Physiology 55, no. 5 (1986): 457–64. http://dx.doi.org/10.1007/bf00421637.

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Sanger, Terence D. "Bayesian Filtering of Myoelectric Signals." Journal of Neurophysiology 97, no. 2 (2007): 1839–45. http://dx.doi.org/10.1152/jn.00936.2006.

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Surface electromyography is used in research, to estimate the activity of muscle, in prosthetic design, to provide a control signal, and in biofeedback, to provide subjects with a visual or auditory indication of muscle contraction. Unfortunately, successful applications are limited by the variability in the signal and the consequent poor quality of estimates. I propose to use a nonlinear recursive filter based on Bayesian estimation. The desired filtered signal is modeled as a combined diffusion and jump process and the measured electromyographic (EMG) signal is modeled as a random process with a density in the exponential family and rate given by the desired signal. The rate is estimated on-line by calculating the full conditional density given all past measurements from a single electrode. The Bayesian estimate gives the filtered signal that best describes the observed EMG signal. This estimate yields results with very low short-time variability but also with the capability of very rapid response to change. The estimate approximates isometric joint torque with lower error and higher signal-to-noise ratio than current linear methods. Use of the nonlinear filter significantly reduces noise compared with current algorithms, and it may therefore permit more effective use of the EMG signal for prosthetic control, biofeedback, and neurophysiology research.
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Topalović, Marko, Đorđe Damnjanović, Aleksandar Peulić, Milan Blagojević, and Nenad Filipović. "SYLLABLE-BASED SPEECH RECOGNITION USING ELECTROMYOGRAPHY AND DECISION SET CLASSIFIER." Biomedical Engineering: Applications, Basis and Communications 27, no. 02 (2015): 1550020. http://dx.doi.org/10.4015/s1016237215500209.

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During the speech, contractions of muscles in the speech apparatus produce myoelectric signals that can be picked up by electrodes, filtered and analyzed. The problem of extraction of speech information from these signals is significant for patients with damaged speech apparatus, such as laryngectomy patients, who could use speech recognition based on myoelectric signal classification to communicate by means of the synthetic speech. In the most previously conducted research, classification is performed on a ten word vocabulary which resulted in a good classification rate. In this paper, a possibility for myoelectric syllable based speech classification is analyzed on a significantly larger vocabulary with novel decision set based classifier which is simple, easy to adapt, convenient for research and similar to the way humans think. In order to have a high quality of recorded myoelectric signals, analysis of the optimal position of electrodes is performed. Classification is performed by comparison between syllable combination and whole words. Based on classification rate, words can belong to easy, medium or hard to distinguish group. Results based on generated list of best matching combinations show that decision set analysis of myoelectric signals for speech recognition is a promising novel method.
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Dissertations / Theses on the topic "Myoelectric signal"

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Liu, Lukai. "A Study of Myoelectric Signal Processing." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/34.

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This dissertation of various aspects of electromyogram (EMG: muscle electrical activity) signal processing is comprised of two projects in which I was the lead investigator and two team projects in which I participated. The first investigator-led project was a study of reconstructing continuous EMG discharge rates from neural impulses. Related methods for calculating neural firing rates in other contexts were adapted and applied to the intramuscular motor unit action potential train firing rate. Statistical results based on simulation and clinical data suggest that performances of spline-based methods are superior to conventional filter-based methods in the absence of decomposition error, but they unacceptably degrade in the presence of even the smallest decomposition errors present in real EMG data, which is typically around 3-5%. Optimal parameters for each method are found, and with normal decomposition error rates, ranks of these methods with their optimal parameters are given. Overall, Hanning filtering and Berger methods exhibit consistent and significant advantages over other methods. In the second investigator-led project, the technique of signal whitening was applied prior to motion classification of upper limb surface EMG signals previously collected from the forearm muscles of intact and amputee subjects. The motions classified consisted of 11 hand and wrist actions pertaining to prosthesis control. Theoretical models and experimental data showed that whitening increased EMG signal bandwidth by 65-75% and the coefficients of variation of temporal features computed from the EMG were reduced. As a result, a consistent classification accuracy improvement of 3-5% was observed for all subjects at small analysis durations (< 100 ms). In the first team-based project, advanced modeling methods of the constant posture EMG-torque relationship about the elbow were studied: whitened and multi-channel EMG signals, training set duration, regularized model parameter estimation and nonlinear models. Combined, these methods reduced error to less than a quarter of standard techniques. In the second team-based project, a study related biceps-triceps surface EMG to elbow torque at seven joint angles during constant-posture contractions. Models accounting for co-contraction estimated that individual flexion muscle torques were much higher than models that did not account for co-contraction.
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Englehart, K. "Signal representation for classification of the transient myoelectric signal." Thesis, University of New Brunswick, 1998. http://hdl.handle.net/1882/808.

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Englehart, Kevin. "Signal representation for classification of the transient myoelectric signal." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0016/NQ46463.pdf.

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Bach, Per Ferdinand. "Myoelectric signal features for upper limb prostheses." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8985.

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<p>In the last couple of years The Institute of Cybernetics at NTNU, Norway, has based its research on the SVEN work carried out in Sweden in the late 1970’s. The SVEN hand was an on/off-controlled upper limb prosthesis based on electromyographic (EMG) signals. This master thesis is a part of the renewed and continuing research. This study will try to identify signal features that are beneficial in a proportional control of a multi-function upper limb prosthesis. The intent is to identify a set of signal features that could be implemented in a practical proportional control system to enhance the movement functions of the prosthesis such that it more closely mimic the movements of a normal upper limb. The data set used in this paper consist of EMG signals and VICON angle data recorded by Fougner (2007). A short explanation will be given on how to acquire such data. A brief introduction on feature selection defines the properties of a wrapper and filter approach in search for a feature subset. Wavelets properties are explained and two wavelet techniques are used in order to obtain more information from the EMG signal in addition to existing features. From this, we search for a subset of features that will let us use a mapping function that estimates a correct motion with respect to the features fed to it. The Cosine Similarity Transform (CST) and the Correlation coefficient (CORR) will in addition to RMSE be investigated in order to find an optimal performance indicator. With a good and reliable indicator we may find a suitable subset. EWC-WAVE were found to be the best subset according to both CST and RMSE. Based upon the information obtained from each performance indicator, it is suggested that CST should be carried out as a measure of accuracy on how to map data in the future. There are still unsolved problems. Some of the angles we tried to estimate with a neural network suffered and produced non-informative data. This indicate that one should add more hidden nodes to a neural network when more features are used as input. We have obtained indications that we do need to combine feature subsets in order to obtain higher accuracy of the estimated signal. It is proposed that a post-processing technique should be developed and used subsequent to the pattern recognition methods in order to achieve a signal that better reflects the estimation and may be used as a control signal for a prosthesis. Hopefully will these findings help improve future work to achieve an enhanced proportional control for a real prosthesis.</p>
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Del, Boca Adrian. "Myoelectric signal recognition using artificial neural networks in real time." FIU Digital Commons, 1993. http://digitalcommons.fiu.edu/etd/2764.

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Application of EMG-controlled functional neuromuscular stimulation to a denervated muscle depends largely on the successful discrimination of the EMG signal by which the subject desires to execute control over the impeded movement. This can be achieved by an adaptive and flexible interface regardless of electrodes location, strength of remaining muscle activity or even personal conditions. Adaptability is a natural and important characteristic of artificial neural networks. This research work is restricted to the development of a real-time application of artificial neural network to the EMG signature recognition. Through this new approach, EMG features extracted by Fourier analysis are presented to a multilayer perceptron type neural network. The neural network learns the most relevant features of the control signal. For real-time operation, a digital signal processor operates over the resulting set of weights from the learning process, and maps the incoming signal to the stimulus control domain. Results showed a highly accurate discrimination of the EMG signal over interference patterns.
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McCool, Paul. "Surface myoelectric signal analysis and enhancement for improved prosthesis control." Thesis, University of Strathclyde, 2014. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=23209.

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In this thesis, novel signal processing and machine learning techniques are presented in the field of myoelectric control. Specifically, algorithms for activity detection, noise identification and noise reduction are introduced, evaluated and discussed. The ultimate aim has been to develop algorithms to improve the performance of prosthetic control systems that use myoelectric signals. Such systems must be an ability to distinguish between electromyographic signals and background noise. For this, the behaviour of One-Dimensional Local Binary Pattern histograms were used to identify the presence of myoelectric activity in recorded signals that originated from electrode sensors on the surface of the skin. This technique was compared against two other activity detection methods and it was found to give better performance in some circumstances. In particular, a lower False Positive Rate was achieved. Noise is always present in myoelectric signals, and if it can be identified then step s can be taken to quantify and/or mitigate it. Pattern recognition was used to identify a single noise type in pre-recorded myoelectric signals. A set of Radial Basis Function Support Vector Machines were trained and tested on clean myoelectric signals that have been artificially contaminated with five typical noise types. The behaviour of the features and the nature of the confusion are discussed. Identification was shown to be possible, but confusion between noise types grew as the SNR increased. Spectral Enhancement, which is normally used on speech signals, is applied to myoelectric signals in an attempt to mitigate noise. Spectral Enhancement based on Improved Minima Controlled Recursive Averaging (IMCRA) was found to improve the classification accuracy, and by corollary the signal quality, with signals that had white noise artificially added (which can be present in recorded myoelectric signals) and with intrinsically noisy signals. The improvement was higher when fewer channels were used.
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Thomson, Kyle E. "Hardware considerations of space-time processing in implantable neuroprosthetic devices." Diss., Connect to online resource - MSU authorized users, 2006.

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Thesis (M.S.)--Michigan State University. Dept. of Electrical and Computer Engineering, 2006.<br>Title from PDF t.p. (viewed on Nov. 20, 2008) Includes bibliographical references (p. 51-52). Also issued in print.
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Hofmann, David [Verfasser], Florentin [Akademischer Betreuer] Wörgötter, and Dario [Akademischer Betreuer] Farina. "Myoelectric Signal Processing for Prosthesis Control / David Hofmann. Gutachter: Florentin Wörgötter ; Dario Farina. Betreuer: Florentin Wörgötter." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2015. http://d-nb.info/1066427496/34.

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Luo, R. "Can the voluntary drive to a paretic muscle be estimated from the myoelectric signal during stimulation?" Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1409755/.

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Patients with SCI sometimes recover lost function after using FES. This phenomenon, known as the carry-over effect, is not fully understood. One theory used to explain this mechanism is that electrical stimulation of the peripheral nerve causes antidromic action potentials to reach the anterior horn cells in time with the patient’s voluntary effort. This may reinforce the motor pathways and consequently restore voluntary control. However, the theory has never been properly tested and testing requires a method of measuring the voluntary drive. This project aims to find out whether it is possible to estimate the voluntary drive from measured myoelectric signals. The project is based on an FES cycling system with the ability to adjust the stimulation intensity relating to the corresponding voluntary drive. In paretic muscles, the weak voluntary contraction produces an EMG response. The EMG signal cannot be used directly as an indication of the voluntary drive because of the presence of stimulus artefact and reflexes. Two methods were investigated to estimate the voluntary drive. A time domain method was tested using RMS EMG extracted from a range of time windows following the stimulation pulse. This approach was unsatisfactory because the large variations seen in the RMS EMG amplitudes for the same power output as well as the low sensitivity of it to the change of power output. A frequency domain approach was then tested using coherence between co-contracting muscles. It was encouraging to see that the area under the coherence curve in the β band reflected changes in the power output level. However, further tests showed that this area was also greatly influenced by exercise time, becoming unpredictable after 3 minutes. In conclusion, neither of the two methods of using the myoelectric signal from muscles under stimulation is practical for the estimation of voluntary drive.
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Michel, Aubé. "Influence of pedalling rate and resistance on the deterministic component of the myoelectric signal during ergometer cycling." Thesis, University of New Brunswick, 1997. http://hdl.handle.net/1882/496.

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Books on the topic "Myoelectric signal"

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Grant, Rob. Myoelectric signal processing for control of limb prostheses. National Library of Canada = Bibliothèque nationale du Canada, 1993.

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Dempsey, George John. Modelling the musculo-skeletal system using myoelectic signals. The Author], 1988.

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

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Chaiyaratana, N. Myoelectric signals pattern recognition for intelligent functional operation of upper-limb prosthesis. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

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Clark, Jane E. The efficacy of using human myoelectric signals to control the limbs of robots in space. National Aeronautics and Space Administration, 1988.

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Koch, Kenneth L., and Robert M. Stern, eds. Handbook of Electrogastrography. Oxford University Press, 2003. http://dx.doi.org/10.1093/oso/9780195147889.001.0001.

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The Handbook of Electrogastrography is the first textbook dedicated to reviewing the physiology of gastric myelectrical activity and the measurement of this electrical activity with electrodes placed on the abdominal surface - the electrogastrogram. The Handbook is divided into three major sections. The first section (Chapters 1-3) focuses on the history of electrogastrography, electrical activity of the interstitial cells of Cajal, the cells from which gastric electrical rhythmicity emanates. The cellular level of gastric electrical rhythmicity provides an understanding of the physiological basis of the electrogastrogram signal. The second major section of the book (Chapters 4-6) incorporates the practical aspects of recording a high quality electrogastrogram and approaches to the analysis of the electrogastrogram using visual inspection and computer techniques. This section focuses on the authors' combined experience of examining EGG recordings for more than sixty years. From this rich research and clinical experience, the clinical application of EGG recordings in an approach to patients with unexplained nausea and vomiting is described. Neuromuscular disorders of the stomach involving gastric dysrhythmias are reviewed. The third major section of the book (Chapters 7-9) comprises many examples of gastric dysrythmias ranging from bradygastrias to tachygastrias and mixed dysrythmias. Current understanding of the mechanisms of gastric dysrhythmias is reviewed. Artifacts in the EGG signal, which may be confused with gastric dysrhythmias, are also presented. The Handbook of Electrogastrography will be a valuable reference for physicians interested in recording gastric electrical activity in clinical practices or in clinical research. Gastroenterologists, internists, psychologists and others with an interest in gastric myoelectrical events will also find extensive and relevant information for recording and interpreting EGGs in the Handbook.
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Book chapters on the topic "Myoelectric signal"

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Lovely, D. F. "Signals and Signal Processing for Myoelectric Control." In Powered Upper Limb Prostheses. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18812-1_3.

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Gaßner, Philip, and Klaus Buchenrieder. "Improved Classification of Myoelectric Signals by Using Normalized Signal Trains." In Computer Aided Systems Theory – EUROCAST 2019. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45096-0_46.

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Lovely, D. F. "The Origins and Nature of the Myoelectric Signal." In Powered Upper Limb Prostheses. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18812-1_2.

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Priadythama, I., and S. Susmartini. "Preliminary Study on Frequency Based Parameters of Myoelectric Signal Using Single Channel Myoelectric Module." In 7th WACBE World Congress on Bioengineering 2015. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19452-3_38.

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Gallant, Peter J., Evelyn L. Morin, and Lloyd E. Peppard. "Improving myoelectric signal classifier generalization by preprocessing with exploratory projections." In Information Theory and Applications II. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0025150.

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Zapana, Ulises Gordillo, Renée M. Condori Apaza, Nancy I. Orihuela Ordoñez, and Alfredo Cárdenas Rivera. "Prototype Upper Limb Prosthetic Controlled by Myoelectric Signals Using a Digital Signal Processor Platform." In Interdisciplinary Applications of Kinematics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10723-3_16.

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He, Jiayuan, Dingguo Zhang, and Xiangyang Zhu. "Adaptive Pattern Recognition of Myoelectric Signal towards Practical Multifunctional Prosthesis Control." In Intelligent Robotics and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33509-9_52.

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Schill, Oliver, R. Rupp, and M. Reischl. "Signal processing concepts for optimal myoelectric sensor placement in a modular hybrid FES orthosis." In IFMBE Proceedings. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89208-3_433.

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Amin, Praahas, and Airani Mohammad Khan. "A Study on the Effect of Dimensionality Reduction on Classification Accuracy of Myoelectric Control Systems." In Advances in VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0443-0_3.

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Herrmann, Stefan, Andreas Attenberger, and Klaus Buchenrieder. "Prostheses Control with Combined Near-Infrared and Myoelectric Signals." In Computer Aided Systems Theory – EUROCAST 2011. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27579-1_77.

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Conference papers on the topic "Myoelectric signal"

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Bingham, Jeffrey T., and Marco P. Schoen. "Characterization of Myoelectric Signals Using System Identification Techniques." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59904.

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Human muscle motion is initiated in the central nervous system where a nervous signal travels through the body and the motor neurons excite the muscles to move. These signals, termed myoelectric signals, can be measured on the surface of the skin as an electrical potential. By analyzing these signals it is possible to determine the muscle actions the signals elicit, and thus can be used in manipulating smart prostheses and teleoperation of machinery. Due to the randomness of myoelectric signals, identification of the signals is not complete, therefore the goal of this project is to complete a study of the characterization of one set of hand motions using current system identification methods. The gripping motion of the hand and the corresponding myoelectric signals are measured and the data captured with a personal computer. Using computer software the captured data are processed and finally subjected to several system identification routines. Using this technique it is possible to construct a mathematical model that correlates the myoelectric signals with the matching hand motion.
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Partridge, Donna, Calvin Lee, Tod Pinniger, and Warren Ogle. "Speech through myoelectric signal recognition SMyLES." In the 28th annual Southeast regional conference. ACM Press, 1990. http://dx.doi.org/10.1145/98949.99151.

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Patel, P., S. Allen, and G. Rapach. "Microcontroller based prosthetic device using myoelectric signal." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.95259.

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Inoue, Shu, Masahiro Oya, and Hidetaka Ohta. "Finger Joint Dynamics with Myoelectric Signal inputs." In 2018 International Conference on Information and Communication Technology Robotics (ICT-ROBOT). IEEE, 2018. http://dx.doi.org/10.1109/ict-robot.2018.8549888.

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Ooe, Katsutoshi, Reina Kishimoto, Masahiro Nakajima, Kosuke Sekiyama, and Toshio Fukuda. "Controllable artificial larynx using neck myoelectric signal." In 2012 International Symposium on Micro-NanoMechatronics and Human Science (MHS). IEEE, 2012. http://dx.doi.org/10.1109/mhs.2012.6492414.

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Khatik, Raj, and Vijaypal Yadav. "Myoelectric Signal Based Multiple Grip Pattern Prosthetic Arm." In 2018 6th Edition of International Conference on Wireless Networks & Embedded Systems (WECON). IEEE, 2018. http://dx.doi.org/10.1109/wecon.2018.8782069.

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Rahmatillah, Akif, and Azisya Amalia Karimasari. "Prototype of arm therapy device using myoelectric signal." In 2017 International Seminar on Sensors, Instrumentation, Measurement and Metrology (ISSIMM). IEEE, 2017. http://dx.doi.org/10.1109/issimm.2017.8124254.

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Ooe, Katsutoshi, Carlos Rafael, Tercero Villagran, Kosuke Sekiyama, and Toshio Fukuda. "Speech assistance devices controlled by neck myoelectric signal." In 2011 International Symposium on Micro-NanoMechatronics and Human Science (MHS). IEEE, 2011. http://dx.doi.org/10.1109/mhs.2011.6102200.

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He, Jianrong, Xin'an Wang, Xing Zhang, Bo Wang, Qiuping Li, and Changpei Qiu. "Unvoiced Speech Recognition Algorithm Based on Myoelectric Signal." In ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing. ACM, 2020. http://dx.doi.org/10.1145/3383972.3384029.

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PonPriya, P., and E. Priya. "Design and control of prosthetic hand using myoelectric signal." In 2017 2nd International Conference on Computing and Communications Technologies (ICCCT). IEEE, 2017. http://dx.doi.org/10.1109/iccct2.2017.7972314.

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Reports on the topic "Myoelectric signal"

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Chan, A. D., K. Englehart, B. Hudgins, and D. F. Lovely. Hidden Markov Model Classification of Myoelectric Signals in Speech. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada410037.

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