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Journal articles on the topic 'Myoelectric signal'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Merletti, R., S. H. Roy, E. Kupa, S. Roatta, and A. Granata. "Modeling of surface myoelectric signals. II. Model-based signal interpretation." IEEE Transactions on Biomedical Engineering 46, no. 7 (1999): 821–29. http://dx.doi.org/10.1109/10.771191.

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12

Broman, H. "Knowledge-based signal processing in the decomposition of myoelectric signals." IEEE Engineering in Medicine and Biology Magazine 7, no. 2 (1988): 24–28. http://dx.doi.org/10.1109/51.1970.

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13

Guo, Benzhen, Yanli Ma, Jingjing Yang, Zhihui Wang, and Xiao Zhang. "Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation." Computational Intelligence and Neuroscience 2020 (December 28, 2020): 1–12. http://dx.doi.org/10.1155/2020/8846021.

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Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric signals are collected on six motions of eight subjects’ upper limbs. A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition. The offline and online performance of the two models are then compared. The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM. The robotic arm control accuracy is (88.5 ± 5.5)%. Significance analysis shows no significant correlation ( p = 0.056) among real-time control, offline testing, and online testing. The Lw-CNN model performs well in the recognition of upper-limb motion intents and can realize real-time control of a commercial robotic arm.

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14

Pantall, Annette, Emma F. Hodson-Tole, Robert J. Gregor, and Boris I. Prilutsky. "Increased intensity and reduced frequency of EMG signals from feline self-reinnervated ankle extensors during walking do not normalize excessive lengthening." Journal of Neurophysiology 115, no. 5 (2016): 2406–20. http://dx.doi.org/10.1152/jn.00565.2015.

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Kinematics of cat level walking recover after elimination of length-dependent sensory feedback from the major ankle extensor muscles induced by self-reinnervation. Little is known, however, about changes in locomotor myoelectric activity of self-reinnervated muscles. We examined the myoelectric activity of self-reinnervated muscles and intact synergists to determine the extent to which patterns of muscle activity change as almost normal walking is restored following muscle self-reinnervation. Nerves to soleus (SO) and lateral gastrocnemius (LG) of six adult cats were surgically transected and repaired. Intramuscular myoelectric signals of SO, LG, medial gastrocnemius (MG), and plantaris (PL), muscle fascicle length of SO and MG, and hindlimb mechanics were recorded during level and slope (±27°) walking before and after (10–12 wk postsurgery) self-reinnervation of LG and SO. Mean myoelectric signal intensity and frequency were determined using wavelet analysis. Following SO and LG self-reinnervation, mean myoelectric signal intensity increased and frequency decreased in most conditions for SO and LG as well as for intact synergist MG ( P < 0.05). Greater elongation of SO muscle-tendon unit during downslope and unchanged magnitudes of ankle extensor moment during the stance phase in all walking conditions suggested a functional deficiency of ankle extensors after self-reinnervation. Possible effects of morphological reorganization of motor units of ankle extensors and altered sensory and central inputs on the changes in myoelectric activity of self-reinnervated SO and LG are discussed.

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15

Pacheco, Matheus M., Renato Moraes, Tenysson W. Lemos, Raoul M. Bongers, and Go Tani. "Convergence in myoelectric control: Between individual patterns of myoelectric learning." Biomedical Signal Processing and Control 70 (September 2021): 103057. http://dx.doi.org/10.1016/j.bspc.2021.103057.

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16

Al-Assaf, Y., and H. Al-Nashash. "Surface myoelectric signal classification for prostheses control." Journal of Medical Engineering & Technology 29, no. 5 (2005): 203–7. http://dx.doi.org/10.1080/03091900412331289906.

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17

Xiong, Fuqin Q., and Ed Shwedyk. "Some Aspects of Nonstationary Myoelectric Signal Processing." IEEE Transactions on Biomedical Engineering BME-34, no. 2 (1987): 166–72. http://dx.doi.org/10.1109/tbme.1987.326041.

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18

Ray, G. C. "Myoelectric Signal—Its Analysis Modelling and Use." IETE Technical Review 11, no. 1 (1994): 15–22. http://dx.doi.org/10.1080/02564602.1994.11437413.

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19

Perez, Jorge, Hilda N. Ferrao, and G. E. Juarez. "Myoelectric Signal Processing Using Time-Frequency Distribution." IEEE Latin America Transactions 11, no. 1 (2013): 246–50. http://dx.doi.org/10.1109/tla.2013.6502811.

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20

OE, Katsutoshi, and Shoya UNO. "Control Method of Electrolarynx with Myoelectric Signal." Proceedings of Mechanical Engineering Congress, Japan 2020 (2020): J10302. http://dx.doi.org/10.1299/jsmemecj.2020.j10302.

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21

Oe, Katsutoshi. "An Electrolarynx Control Method Using Myoelectric Signals from the Neck." Journal of Robotics and Mechatronics 33, no. 4 (2021): 804–13. http://dx.doi.org/10.20965/jrm.2021.p0804.

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Patients who have lost vocal cord function due to laryngeal cancer or laryngeal injury are incapable of speech because it is impossible to generate the laryngeal tone from which the voice originates. For such patients, various speech production substitutes have been devised and put into practical use. The electrolarynx is one of these speech production substitutes and it can be used with relative ease. However, the sound is sometimes difficult to hear and its quality is monotonous. Therefore, focusing on the control method to improve the articulation of the electrolarynx, we have proposed an electrolarynx controlled by myoelectric signals of the neck. The sternohyoid muscle, which is located in the superficial layer of the neck, was the source of the myoelectric signals. This muscle is active during speech, and its activity increases mainly at the time of speech in a low voice. We succeeded in detecting the surface myoelectric signals of the sternohyoid muscle and performing on/off control of the electrolarynx by signal processing. This report includes the derivation of a control function for converting into a control signal of the fundamental frequency of the electrolarynx from the relationship between the myoelectric signals and the fundamental frequency of the voice. This report also includes an evaluation of the controllability of the electrolarynx by comparing the obtained control signal with the user’s intention. Regarding the control of the fundamental frequency, we have proposed a method of control in three stages – high, medium, and low – and a method of control in two stages – high and low – and compared their performances. The results of the three-stage control indicated that the use of the logarithm as a control function for converting the myoelectric signals into the fundamental frequency of the electrolarynx succeeded in the control at an accuracy of 90% or more by changing the pitch of the generated sound depending on the subjects. It was also indicated that the error rate was as low as less than 20%, while maintaining a constant sound. This makes it clear that the use of the logarithm as a control function gives the highest controllability. The two-stage control exhibits a very high control success rate exceeding 90%, regardless of the type of control function; in particular, the control function using the logarithm exhibits a control success rate exceeding 95%. These results indicate that the electrolarynx control function obtained using the logarithmic function has excellent controllability.

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22

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

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Several methods have been developed in biomedical signal processing to extract the envelope and features of electromyography (EMG) signals and predict human motion. Also, efforts were made to use this information to improve the interaction of a human body and artificial protheses. The main operations here are envelope acquiring, artifacts filtering, estimate smoothing, EMG value standardizing, feature classifying, and motion recognizing. In this paper, we employ EMG data to extract the envelope with a highest Gaussianity using the rectified signal, where we deal with the absolute EMG signals so that all values become positive. First, we remove artifacts from EMG data by using filters such as the Kalman filter (KF), H1 filter, unbiased finite impulse response (UFIR) filter, and the cKF, cH1 filter, and cUFIR filter modified for colored measurement noise. Next, we standardize the EMG envelope and improve the Gaussianity. Finally, we extract the EMG signal features to provide an accurate prediction.

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23

Welinder, Annika, Leif Sörnmo, Dirk Q. Feild, et al. "Comparison of Signal Quality Between Easi and Mason-Likar 12-Lead Electrocardiograms During Physical Activity." American Journal of Critical Care 13, no. 3 (2004): 228–34. http://dx.doi.org/10.4037/ajcc2004.13.3.228.

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• Background Myoelectric noise and baseline wander, artifacts that appear when patients move during electrocardiographic monitoring, can cause false alarms. This problem can be addressed by using a reduced lead set and placing electrodes on the anterior part of the torso only. The Mason-Likar modification of the standard 12-lead electrocardiogram and the EASI lead system are 2 alternative systems for lead placement. • Objectives To test the hypothesis that the EASI lead system is less susceptible to artifacts than is the Mason-Likar modification of the standard 12-lead electrocardiogram. • Methods Baseline wander and myoelectric noise amplitudes of EASI and Mason-Likar 12-lead electrocardiograms were compared. Twenty healthy volunteers participated. Both lead systems were recorded simultaneously for different types of physical activities. For each lead in each subject, baseline wander and myoelectric noise were measured for both systems, at rest and during each physical activity. • Results The outcome for baseline wander was mixed. For myoelectric noise content, the EASI system performed better for the limb leads in the different physical activities. In the precordial leads, the differences were minimal or mixed. However, for supine-to-right turning, EASI performed worse than the Mason-Likar system. • Conclusions The 2 systems have similar susceptibilities to baseline wander. The EASI system is, however, less susceptible to myoelectric noise than is the Mason-Likar system. EASI performed worse than Mason-Likar for turning supine to right, because only the EASI system uses an electrode in the right-midaxillary line.

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24

McLean, L., M. Tingley, R. N. Scott, and J. Rickards. "Myoelectric signal measurement during prolonged computer terminal work." Journal of Electromyography and Kinesiology 10, no. 1 (2000): 33–45. http://dx.doi.org/10.1016/s1050-6411(99)00021-8.

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25

Scheme, Erik J., Bernard Hudgins, and Phillip A. Parker. "Myoelectric Signal Classification for Phoneme-Based Speech Recognition." IEEE Transactions on Biomedical Engineering 54, no. 4 (2007): 694–99. http://dx.doi.org/10.1109/tbme.2006.889175.

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26

ARJUNAN, SRIDHAR P., DINESH K. KUMAR, and BIJAYA K. PANIGRAHI. "RECOGNITION OF FINGER/HAND GRIP MECHANISM BY COMPUTING S-TRANSFORM FEATURES OF SURFACE ELECTROMYOGRAM SIGNAL FROM HEALTHY AND AMPUTEE." Journal of Mechanics in Medicine and Biology 16, no. 06 (2016): 1650076. http://dx.doi.org/10.1142/s0219519416500767.

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Accurate identification of intended grip actions using the myoelectric signal recorded from the surface of the residual muscles can facilitate natural control of a prosthetic hand for an amputee. However, this is not trivial due to the complexity of the hand muscles. To overcome these shortcomings, there is the need for determining features of the myoelectric recordings that can be used for accurate identification of the grip actions. This study reports the use of S-transform (ST) of the surface myoelectric recordings for recognizing the intent of the user to generate a set of grip patterns. Surface Electromyogram (sEMG) recorded while performing five different hand/finger grip patterns was analyzed. ST of the signal was computed to analyze the signal in a windowed time–frequency domain. The energy and mean amplitude of the transformed signal were classified using a neural network. The method was tested for able-hand and trans-radial amputee subjects. The results show that ST showed improved sensitivity, specificity and accuracy for both healthy and amputee people.

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27

Naik, Ganesh, and Dinesh Kumar. "Hybrid Feature Selection for Myoelectric Signal Classification Using MICA." Journal of Electrical Engineering 61, no. 2 (2010): 93–99. http://dx.doi.org/10.2478/v10187-010-0013-8.

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Hybrid Feature Selection for Myoelectric Signal Classification Using MICA This paper presents a novel method to enhance the performance of Independent Component Analysis (ICA) of myoelectric signal by decomposing the signal into components originating from different muscles. First, we use Multi run ICA (MICA) algorithm to separate the muscle activities. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Testing was conducted using several single shot experiments conducted with five subjects. The results indicate that the system is able to classify four different wrist actions with near 100% accuracy.

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28

Torres, Fernando, Santiago Puente, and Andrés Úbeda. "Assistance Robotics and Biosensors." Sensors 18, no. 10 (2018): 3502. http://dx.doi.org/10.3390/s18103502.

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This Special Issue is focused on breakthrough developments in the field of biosensors and current scientific progress in biomedical signal processing. The papers address innovative solutions in assistance robotics based on bioelectrical signals, including: Affordable biosensor technology, affordable assistive-robotics devices, new techniques in myoelectric control and advances in brain–machine interfacing.

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29

Broman, H., G. Bilotto, and C. J. De Luca. "Myoelectric signal conduction velocity and spectral parameters: influence of force and time." Journal of Applied Physiology 58, no. 5 (1985): 1428–37. http://dx.doi.org/10.1152/jappl.1985.58.5.1428.

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Reports on measurement of muscle fiber conduction velocity in humans are scarce. Inferences on the behavior of conduction velocity have been drawn from the behavior of myoelectric spectral parameters. The present report contains information on conduction velocity and spectral parameters studied at various muscle contraction levels and during and after sustained contractions. The following results have been obtained from measurements on the tibialis anterior muscle. Conduction velocity demonstrated a positive correlation with limb circumference and with muscle force output. Thus we conclude that the diameters of the muscle fibers of high-threshold motor units are, on an average, larger than those of low-threshold motor units. The study of a sustained contraction and on the recovery after such a contraction revealed that conduction velocity consistently decreased during a strong contraction as did various myoelectric spectral parameters. However, the spectral parameters decreased approximately twice as much as did the conduction velocity, and we conclude that factors other than the conduction velocity along the muscle fibers affect the myoelectric signal during a high-level contraction. These other factors appertain to changes in the firing statistics of individual motor units as well as the correlation between the firings of different motor units.

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30

Ando, Takeshi, Masaki Watanabe, Keigo Nishimoto, Yuya Matsumoto, Masatoshi Seki, and Masakatsu G. Fujie. "Myoelectric-Controlled Exoskeletal Elbow Robot to Suppress Essential Tremor: Extraction of Elbow Flexion Movement Using STFTs and TDNN." Journal of Robotics and Mechatronics 24, no. 1 (2012): 141–49. http://dx.doi.org/10.20965/jrm.2012.p0141.

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Essential tremor is the most common of all involuntary movements. Many patients with an upper-limb tremor have serious difficulties in performing daily activities. We developed a myoelectric-controlled exoskeletal robot to suppress tremor. In this article, we focus on developing a signal processing method to extract voluntary movement from a myoelectric in which the voluntary movement and tremor were mixed. First, a Low-Pass Filter (LPF) and Neural Network (NN) were used to recognize the tremor patient’s movement. Using these techniques, it was difficult to recognize the movement accurately because the myoelectric signal of the tremor patient periodically oscillated. Then, Short-Time Fourier Transformation (STFT) and NN were used to recognize the movement. This method was more suitable than LPF and NN. However, the recognition timing at the start of the movement was late. Finally, a hybrid algorithm for using both short and long windows’ STFTs, which is a kind of “mixture of experts,” was proposed and developed. With this type of signal processing, elbow flexion was accurately recognized without the time delay in starting the movement.

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31

Wakeling, James M., Motoshi Kaya, Genevieve K. Temple, Ian A. Johnston, and Walter Herzog. "Determining patterns of motor recruitment during locomotion." Journal of Experimental Biology 205, no. 3 (2002): 359–69. http://dx.doi.org/10.1242/jeb.205.3.359.

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SUMMARY Motor units are the functional units of muscle contraction in vertebrates. Each motor unit comprises muscle fibres of a particular fibre type and can be considered as fast or slow depending on its fibre-type composition. Motor units are typically recruited in a set order, from slow to fast, in response to the force requirements from the muscle. The anatomical separation of fast and slow muscle in fish permits direct recordings from these two fibre types. The frequency spectra from different slow and fast myotomal muscles were measured in the rainbow trout Oncorhynchus mykiss. These two muscle fibre types generated distinct low and high myoelectric frequency bands. The cat paw-shake is an activity that recruits mainly fast muscle. This study showed that the myoelectric signal from the medial gastrocnemius of the cat was concentrated in a high frequency band during paw-shake behaviour. During slow walking, the slow motor units of the medial gastrocnemius are also recruited, and this appeared as increased muscle activity within a low frequency band. Therefore, high and low frequency bands could be distinguished in the myoelectric signals from the cat medial gastrocnemius and probably corresponded, respectively, to fast and slow motor unit recruitment. Myoelectric signals are resolved into time/frequency space using wavelets to demonstrate how patterns of motor unit recruitment can be determined for a range of locomotor activities.

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Lee, Seulah, Babar Jamil, Sunhong Kim, and Youngjin Choi. "Fabric Vest Socket with Embroidered Electrodes for Control of Myoelectric Prosthesis." Sensors 20, no. 4 (2020): 1196. http://dx.doi.org/10.3390/s20041196.

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Myoelectric prostheses assist users to live their daily lives. However, the majority of users are primarily confined to forearm amputees because the surface electromyography (sEMG) that understands the motion intents should be acquired from a residual limb for control of the myoelectric prosthesis. This study proposes a novel fabric vest socket that includes embroidered electrodes suitable for a high-level upper amputee, especially for shoulder disarticulation. The fabric vest socket consists of rigid support and a fabric vest with embroidered electrodes. Several experiments were conducted to verify the practicality of the developed vest socket with embroidered electrodes. The sEMG signals were measured using commercial Ag/AgCl electrodes for a comparison to verify the performance of the embroidered electrodes in terms of signal amplitudes, the skin-electrode impedance, and signal-to-noise ratio (SNR). These results showed that the embroidered electrodes were as effective as the commercial electrodes. Then, posture classification was carried out by able-bodied subjects for the usability of the developed vest socket. The average classification accuracy for each subject reached 97.92%, and for all the subjects it was 93.2%. In other words, the fabric vest socket with the embroidered electrodes could measure sEMG signals with high accuracy. Therefore, it is expected that it can be readily worn by high-level amputees to control their myoelectric prostheses, as well as it is cost effective for fabrication as compared with the traditional socket.

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Ando, Takeshi, Jun Okamoto, Mitsuru Takahashi, and Masakatsu G. Fujie. "Response Evaluation of Rollover Recognition in Myoelectric Controlled Orthosis Using Pneumatic Rubber Muscle for Cancer Bone Metastasis Patient." Journal of Robotics and Mechatronics 23, no. 2 (2011): 302–9. http://dx.doi.org/10.20965/jrm.2011.p0302.

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The myoelectric controlled rollover support orthosis we have been developing for use in bone cancer metastasis requires high accuracy and quick response in signal processing to recognize movement. We quantitatively evaluated the response performance of recognizing rollover using our original Micro Macro Neural Network (MMNN) algorithm. Required response time was calculated as 60 ms by measuring contraction time for the muscle used in the orthosis to support rollover. TheMMNN recognized rollover 65 ms before it started. Rollover was recognized 5 ms after a myoelectric signal was generated, so the MMNN response was sufficient for the muscle to finish contraction before rollover started.

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34

Ning, Naiqiao, and Yong Tang. "Evaluation of an Information Flow Gain Algorithm for Microsensor Information Flow in Limber Motor Rehabilitation." Complexity 2021 (March 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/6638038.

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This paper conducts an evaluative study on the rehabilitation of limb motor function by using a microsensor information flow gain algorithm and investigates the surface electromyography (EMG) signals of the upper limb during rehabilitation training. The surface EMG signals contain a large amount of limb movement information. By analysing and processing the surface EMG signals, we can grasp the human muscle movement state and identify the human upper limb movement intention. The EMG signals were processed by the trap and filter combination denoising method and wavelet denoising method, respectively, the signal-to-noise ratio was used to evaluate the noise reduction effect, and finally, the wavelet denoising method with a better noise reduction effect was selected to process all the EMG signals. After the noise is removed, the signal is extracted in the time domain and frequency domain, and the root mean square (RMS), absolute mean, median frequency in the time domain, and average power frequency in the frequency domain are selected and input to the classifier for pattern recognition. The support vector machine is used to classify the myoelectric signals and optimize the parameters in the support vector machine using the grid search method and particle swarm optimization algorithm and classify the test samples using the trained support vector machine. Compared with the classification results of the grid search optimized support vector machine, the optimized vector machine has a 7% higher recognition rate, reaching 85%. The action recognition classification method of myoelectric signals is combined with an upper limb rehabilitation training platform to verify the feasibility of using myoelectric signals for rehabilitation training. After the classifier recognizes the upper limb movements, the upper computer sends movement commands to the controller to make the rehabilitation platform move according to the recognition results, and finally, the movement execution accuracy of the rehabilitation platform reaches 80% on average.

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35

Norris, Jason A., Kevin B. Englehart, and Dennis F. Lovely. "Myoelectric signal compression using zero-trees of wavelet coefficients." Medical Engineering & Physics 25, no. 9 (2003): 739–46. http://dx.doi.org/10.1016/s1350-4533(03)00118-8.

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36

Ooe, Katsutoshi. "Development of Controllable Artificial Larynx by Neck Myoelectric Signal." Procedia Engineering 47 (2012): 869–72. http://dx.doi.org/10.1016/j.proeng.2012.09.285.

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37

Stashuk, D. W., and R. K. Naphan. "Probabilistic inference-based classification applied to myoelectric signal decomposition." IEEE Transactions on Biomedical Engineering 39, no. 4 (1992): 346–55. http://dx.doi.org/10.1109/10.126607.

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38

Englehart, K. B., and P. A. Parker. "Single motor unit myoelectric signal analysis with nonstationary data." IEEE Transactions on Biomedical Engineering 41, no. 2 (1994): 168–80. http://dx.doi.org/10.1109/10.284928.

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39

Helal, J. N., and J. Duchene. "A pseudoperiodic model for myoelectric signal during dynamic exercise." IEEE Transactions on Biomedical Engineering 36, no. 11 (1989): 1092–97. http://dx.doi.org/10.1109/10.40816.

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40

Hargrove, L. J., K. Englehart, and B. Hudgins. "A Comparison of Surface and Intramuscular Myoelectric Signal Classification." IEEE Transactions on Biomedical Engineering 54, no. 5 (2007): 847–53. http://dx.doi.org/10.1109/tbme.2006.889192.

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41

O'Neill, P., E. L. Morin, and R. N. Scott. "Myoelectric signal characteristics from muscles in residual upper limbs." IEEE Transactions on Rehabilitation Engineering 2, no. 4 (1994): 266–70. http://dx.doi.org/10.1109/86.340871.

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42

OE, Katsutoshi, and Naoto IMAMURA. "Training System for Esophageal Speech Method with Myoelectric Signal." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2020 (2020): 2P2—E19. http://dx.doi.org/10.1299/jsmermd.2020.2p2-e19.

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43

Memberg, William D., Thomas G. Stage, and Robert F. Kirsch. "A Fully Implanted Intramuscular Bipolar Myoelectric Signal Recording Electrode." Neuromodulation: Technology at the Neural Interface 17, no. 8 (2014): 794–99. http://dx.doi.org/10.1111/ner.12165.

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44

Kelly, M. F., P. A. Parker, and R. N. Scott. "Neural network classification of myoelectric signal for prosthesis control." Journal of Electromyography and Kinesiology 1, no. 4 (1991): 229–36. http://dx.doi.org/10.1016/1050-6411(91)90009-t.

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45

Rasool, Ghulam, Nidhal Bouaynaya, Kamran Iqbal, and Gannon White. "Surface myoelectric signal classification using the AR-GARCH model." Biomedical Signal Processing and Control 13 (September 2014): 327–36. http://dx.doi.org/10.1016/j.bspc.2014.06.001.

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46

Parker, P., K. Englehart, and B. Hudgins. "Myoelectric signal processing for control of powered limb prostheses." Journal of Electromyography and Kinesiology 16, no. 6 (2006): 541–48. http://dx.doi.org/10.1016/j.jelekin.2006.08.006.

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47

Moritani, Toshio, Masuo Muro, and Singo Oda. "Myoelectric signal characteristics in lumbar back muscles during fatigue." International Journal of Industrial Ergonomics 9, no. 2 (1992): 151–56. http://dx.doi.org/10.1016/0169-8141(92)90112-d.

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48

Zhang, Y. T., P. A. Parker, and R. N. Scott. "Control performance characteristics of myoelectric signal with additive interference." Medical & Biological Engineering & Computing 29, no. 1 (1991): 84–88. http://dx.doi.org/10.1007/bf02446301.

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Jeyaraj, Pandia Rajan, and Edward Rajan Samuel Nadar. "Adaptive machine learning algorithm employed statistical signal processing for classification of ECG signal and myoelectric signal." Multidimensional Systems and Signal Processing 31, no. 4 (2020): 1299–316. http://dx.doi.org/10.1007/s11045-020-00710-7.

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50

Merletti, R., M. Knaflitz, and C. J. De Luca. "Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions." Journal of Applied Physiology 69, no. 5 (1990): 1810–20. http://dx.doi.org/10.1152/jappl.1990.69.5.1810.

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The time course of muscle fiber conduction velocity and surface myoelectric signal spectral (mean and median frequency of the power spectrum) and amplitude (average rectified and root-mean-square value) parameters was studied in 20 experiments on the tibialis anterior muscle of 10 healthy human subjects during sustained isometric voluntary or electrically elicited contractions. Voluntary contractions at 20% maximal voluntary contraction (MVC) and at 80% MVC with duration of 20 s were performed at the beginning of each experiment. Tetanic electrical stimulation was then applied to the main muscle motor point for 20 s with surface electrodes at five stimulation frequencies (20, 25, 30, 35, and 40 Hz). All subjects showed myoelectric manifestations of muscle fatigue consisting of negative trends of spectral variables and conduction velocity and positive trends of amplitude variables. The main findings of this work are 1) myoelectric signal variables obtained from electrically elicited contractions show fluctuations smaller than those observed in voluntary contractions, 2) spectral variables are more sensitive to fatigue than conduction velocity and the average rectified value is more sensitive to fatigue than the root-mean-square value, 3) conduction velocity is not the only physiological factor affecting spectral variables, and 4) contractions elicited at supramaximal stimulation and frequencies greater than 30 Hz demonstrate myoelectric manifestations of muscle fatigue greater than those observed at 80% MVC sustained for the same time.

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