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1
Zieliński, Grzegorz, and Piotr Gawda. "Surface Electromyography in Dentistry—Past, Present and Future." Journal of Clinical Medicine 13, no. 5 (February 26, 2024): 1328. http://dx.doi.org/10.3390/jcm13051328.
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Surface electromyography (sEMG) is a technique for measuring and analyzing the electrical signals of muscle activity using electrodes placed on the skin’s surface. The aim of this paper was to outline the history of the development and use of surface electromyography in dentistry, to show where research and technical solutions relating to surface electromyography currently lie, and to make recommendations for further research. sEMG is a diagnostic technique that has found significant application in dentistry. The historical section discusses the evolution of sEMG methods and equipment, highlighting how technological advances have influenced the accuracy and applicability of this method in dentistry. The need for standardization of musculoskeletal testing methodology is highlighted and the needed increased technical capabilities of sEMG equipment and the ability to specify parameters (e.g., sampling rates, bandwidth). A higher sampling rate (the recommended may be 2000 Hz or higher in masticatory muscles) allows more accurate recording of changes in the signal, which is essential for accurate analysis of muscle function. Bandwidth is one of the key parameters in sEMG research. Bandwidth determines the range of frequencies effectively recorded by the sEMG system (the recommended frequency limits are usually between 20 Hz and 500 Hz in masticatory muscles). In addition, the increased technical capabilities of sEMG equipment and the ability to specify electromyographic parameters demonstrate the need for a detailed description of selected parameters in the methodological section. This is necessary to maintain the reproducibility of sEMG testing. More high-quality clinical trials are needed in the future.
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Fang, Yinfeng, Honghai Liu, Gongfa Li, and Xiangyang Zhu. "A Multichannel Surface EMG System for Hand Motion Recognition." International Journal of Humanoid Robotics 12, no. 02 (May 27, 2015): 1550011. http://dx.doi.org/10.1142/s0219843615500115.
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Surface electromyography (sEMG)-based hand motion recognition has a variety of promising applications. While a person performs different hand motions, commands can be extracted to control external devices, such as prosthetic hands, tablets and so forth. The acquisition of discriminative sEMG signals determines the accuracy of intended control commands extraction. This paper develops an 16-channel sEMG signal acquisition system with a novel electrode configuration that is specially designed to collect sEMG on the forearm. Besides, to establish the relationship between multichannel sEMG signals and hand motions, a 2D EMG map is designed. Inspired from the electromyographic (EMG) map, this paper proposes an EMG feature named differential root mean square (DRMS) that somewhat takes the relationship between neighboring EMG channels into account. In the task of four hand motion discrimination by K-means and fuzzy C-means, DRMS outperforms traditional root mean square (RMS) by 29.0% and 36.8%, respectively. The findings of this paper support and guide the use of sEMG techniques to investigate sEMG-based hand motion recognition.
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Bolek, Jeffrey E. "Uncommon Surface Electromyography." Biofeedback 38, no. 2 (June 1, 2010): 52–55. http://dx.doi.org/10.5298/1081-5937-38.2.52.
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Abstract Surface electromyography (SEMG) can be used as a tool to help gain the return/discovery of motor function in those with disabilities. This article presents the case of “Joey,” an 18-month-old toddler. An already challenging case due to age is made even more difficult considering his genetically based multiple impairments. SEMG provided a window of opportunity, previously unavailable, to allow Joey to demonstrate the new motor skills that he was capable of learning.
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HE, JINBAO, XINHUA YI, and ZAIFEI LUO. "CHARACTERIZATION OF MOTOR UNIT AT DIFFERENT STRENGTHS WITH MULTI-CHANNEL SURFACE ELECTROMYOGRAPHY." Journal of Mechanics in Medicine and Biology 17, no. 01 (February 2017): 1750024. http://dx.doi.org/10.1142/s0219519417500245.
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In this study, specific changes in electromyographic characteristics of individual motor units (MUs) associated with different muscle contraction forces are investigated using multi-channel surface electromyography (SEMG). The gradient convolution kernel compensation (GCKC) algorithm is employed to separate individual MUs from their surface interferential electromyography (EMG) signals and provide the discharge instants, which is later used in the spike-triggered averaging (STA) techniques to obtain the complete waveform. The method was tested on experimental SEMG signals acquired during constant force contractions of biceps brachii muscles in five subjects. Electromyographic characteristics including the recruitment number, waveform amplitude, discharge pattern and innervation zone (IZ) are studied. Results show that changes in the action potential of single MU with different contraction force levels are consistent with those for all MUs, and that the amplitude of MU action potentials (MUAPs) provides a useful estimate of the muscle contraction forces.
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Arena, John G. "Future Directions in Surface Electromyography." Biofeedback 38, no. 2 (June 1, 2010): 78–82. http://dx.doi.org/10.5298/1081-5937-38.2.78.
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Abstract The use of surface electromyography (SEMG) has increased exponentially in the past four decades. SEMG is one of the most widespread measures employed today in psychophysiological assessment and one of three primary biofeedback modalities. This article briefly outlines three areas that the author believes are important for SEMG to address if it is to continue to flourish in the future: applications in telehealth, the use of telemetry and ambulatory monitoring, and studies on the stability or reliability of surface electromyography.
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Coppeta, Luca, Sandro Gentili, Stefano Mugnaini, Ottavia Balbi, Stefano Massimiani, Gianluca Armieri, Antonio Pietroiusti, and Andrea Magrini. "Neuromuscular Functional Assessment in Low Back Pain by Surface Electromyography (SEMG)." Open Public Health Journal 12, no. 1 (February 28, 2019): 61–67. http://dx.doi.org/10.2174/1874944501912010061.
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Background: Low back pain is a major occupational health issue and a leading cause of disability globally. Significant differences in Surface Electromyography (SEMG) have been reported between persons with Low Back Pain (LBP) and normal, healthy controls. Many studies reveal that when the trunk is in full flexion there is an electrical silence in back muscles referred to as “flexion-relaxation phenomenon.” It is often absent in individuals reporting LBP and particularly chronic LBP. There are several SEMG measures that describe this phenomenon. Objective: To evaluate muscle activity in acute and chronic LBP and the usefulness of quick and reliable procedures to demonstrate abnormal electromyographic activity of the spine erector muscles. Methods: We evaluated 40 subjects aged 25-65 years. For each participant, a clinical history regarding the presence of chronic or acute LBP was collected. Each subject was evaluated with SEMG measures of spine erector muscles during standing and prone position (for acute LBP), and flex-extension movement (for chronic LBP subjects). Superficial potential was recorded and compared between groups. Results: In all three procedures, differences were identified in the surface electromyographic activity between the healthy controls and the one affected by LBP. Conclusion: The study of normal and pathologic electromyographic patterns could be a valid means to support in an objective way the presence/absence of acute and chronic LBP.
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Deprez, Kenneth, Eliah De Baecke, Mauranne Tijskens, Ruben Schoeters, Maarten Velghe, and Arno Thielens. "A Circular, Wireless Surface-Electromyography Array." Sensors 24, no. 4 (February 8, 2024): 1119. http://dx.doi.org/10.3390/s24041119.
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Commercial, high-tech upper limb prostheses offer a lot of functionality and are equipped with high-grade control mechanisms. However, they are relatively expensive and are not accessible to the majority of amputees. Therefore, more affordable, accessible, open-source, and 3D-printable alternatives are being developed. A commonly proposed approach to control these prostheses is to use bio-potentials generated by skeletal muscles, which can be measured using surface electromyography (sEMG). However, this control mechanism either lacks accuracy when a single sEMG sensor is used or involves the use of wires to connect to an array of multiple nodes, which hinders patients’ movements. In order to mitigate these issues, we have developed a circular, wireless s-EMG array that is able to collect sEMG potentials on an array of electrodes that can be spread (not) uniformly around the circumference of a patient’s arm. The modular sEMG system is combined with a Bluetooth Low Energy System on Chip, motion sensors, and a battery. We have benchmarked this system with a commercial, wired, state-of-the-art alternative and found an r = 0.98 (p < 0.01) Spearman correlation between the root-mean-squared (RMS) amplitude of sEMG measurements measured by both devices for the same set of 20 reference gestures, demonstrating that the system is accurate in measuring sEMG. Additionally, we have demonstrated that the RMS amplitudes of sEMG measurements between the different nodes within the array are uncorrelated, indicating that they contain independent information that can be used for higher accuracy in gesture recognition. We show this by training a random forest classifier that can distinguish between 6 gestures with an accuracy of 97%. This work is important for a large and growing group of amputees whose quality of life could be improved using this technology.
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Ankrum, Dennis R. "Questions to ask When Interpreting Surface Electromyography (SEMG) Research." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 30 (July 2000): 5–530. http://dx.doi.org/10.1177/154193120004403036.
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Surface electromyography (SEMG) is widely used to evaluate muscle activity. In SEMG, researchers attach electrodes to the surface of the skin overlying a muscle and measure the amount of electricity it produces as muscle fibers contract. SEMG can determine which muscles are active, their degree of activity, and how active the muscle is compared to the subject's capacity. It can also be used to estimate muscle force. Properly employed, SEMG assists in evaluating the relative risk of a work task. As articles reporting SEMG results are often used by ergonomics practitioners as guidance in job design, the ability to interpret SEMG research is critical. Problems occur when researchers assume their readers have a greater familiarity with SEMG than actually exists, or when they make any of a number of SEMG-related research or interpretation errors. This paper suggests some questions that should be asked when evaluating a study that reports SEMG data.
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Gomez-Correa, Manuela, and David Cruz-Ortiz. "Low-Cost Wearable Band Sensors of Surface Electromyography for Detecting Hand Movements." Sensors 22, no. 16 (August 9, 2022): 5931. http://dx.doi.org/10.3390/s22165931.
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Surface electromyography (sEMG) is a non-invasive measure of electrical activity generated due to muscle contraction. In recent years, sEMG signals have been increasingly used in diverse applications such as rehabilitation, pattern recognition, and control of orthotic and prosthetic systems. This study presents the development of a versatile multi-channel sEMG low-cost wearable band system to acquire 4 signals. In this case, the signals acquired with the proposed device have been used to detect hand movements. However, the WyoFlex band could be used in some sections of the arm or the leg if the section’s diameter matches the diameter of the WyoFlex band. The designed WyoFlex band was fabricated using three-dimensional (3D) printing techniques employing thermoplastic polyurethane and polylactic acid as manufacturing materials. Then, the proposed wearable electromyographic system (WES) consists of 2 WyoFlex bands, which simultaneously allow the wireless acquisition of 4 sEMG channels of each forearm. The collected sEMG can be visualized and stored for future post-processing stages using a graphical user interface designed in Node-RED. Several experimental tests were conducted to verify the performance of the WES. A dataset with sEMG collected from 15 healthy humans has been obtained as part of the presented results. In addition, a classification algorithm based on artificial neural networks has been implemented to validate the usability of the collected sEMG signals.
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Sella, Gabriel E. "Surface EMG (SEMG): A Synopsis." Biofeedback 47, no. 2 (June 1, 2019): 36–43. http://dx.doi.org/10.5298/1081-5937-47.1.05.
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Surface electromyography is an electrophysiological modality assessing the electrical activity of skeletal musculature. The Sella protocol is a structured assessment protocol, including static muscle assessment and dynamic muscle assessment, utilizing standardized electrode placements, conditions, and movements during assessment. This protocol can serve as a basis for designing biofeedback-assisted rehabilitation of patients with chronic pain and other musculoskeletal problems. The protocol can also be applied in forensic evaluations and in optimal performance settings.
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Nacpil, Edric John Cruz, Rencheng Zheng, Tsutomu Kaizuka, and Kimihiko Nakano. "A surface electromyography controlled steering assistance interface." Journal of Intelligent and Connected Vehicles 2, no. 1 (August 29, 2019): 1–13. http://dx.doi.org/10.1108/jicv-11-2018-0011.
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Purpose Two-handed automobile steering at low vehicle speeds may lead to reduced steering ability at large steering wheel angles and shoulder injury at high steering wheel rates (SWRs). As a first step toward solving these problems, this study aims, firstly, to design a surface electromyography (sEMG) controlled steering assistance interface that enables hands-free steering wheel rotation and, secondly, to validate the effect of this rotation on path-following accuracy. Design/methodology/approach A total of 24 drivers used biceps brachii sEMG signals to control the steering assistance interface at a maximized SWR in three driving simulator scenarios: U-turn, 90º turn and 45º turn. For comparison, the scenarios were repeated with a slower SWR and a game steering wheel in place of the steering assistance interface. The path-following accuracy of the steering assistance interface would be validated if it was at least comparable to that of the game steering wheel. Findings Overall, the steering assistance interface with a maximized SWR was comparable to a game steering wheel. For the U-turn, 90º turn and 45º turn, the sEMG-based human–machine interface (HMI) had median lateral errors of 0.55, 0.3 and 0.2 m, respectively, whereas the game steering wheel, respectively, had median lateral errors of 0.7, 0.4 and 0.3 m. The higher accuracy of the sEMG-based HMI was statistically significant in the case of the U-turn. Originality/value Although production automobiles do not use sEMG-based HMIs, and few studies have proposed sEMG controlled steering, the results of the current study warrant further development of a sEMG-based HMI for an actual automobile.
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Piitulainen, Harri, Alberto Botter, Mathieu Bourguignon, Veikko Jousmäki, and Riitta Hari. "Spatial variability in cortex-muscle coherence investigated with magnetoencephalography and high-density surface electromyography." Journal of Neurophysiology 114, no. 5 (November 1, 2015): 2843–53. http://dx.doi.org/10.1152/jn.00574.2015.
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Cortex-muscle coherence (CMC) reflects coupling between magnetoencephalography (MEG) and surface electromyography (sEMG), being strongest during isometric contraction but absent, for unknown reasons, in some individuals. We used a novel nonmagnetic high-density sEMG (HD-sEMG) electrode grid (36 mm × 12 mm; 60 electrodes separated by 3 mm) to study effects of sEMG recording site, electrode derivation, and rectification on the strength of CMC. Monopolar sEMG from right thenar and 306-channel whole-scalp MEG were recorded from 14 subjects during 4-min isometric thumb abduction. CMC was computed for 60 monopolar, 55 bipolar, and 32 Laplacian HD-sEMG derivations, and two derivations were computed to mimic “macroscopic” monopolar and bipolar sEMG (electrode diameter 9 mm; interelectrode distance 21 mm). With unrectified sEMG, 12 subjects showed statistically significant CMC in 91–95% of the HD-sEMG channels, with maximum coherence at ∼25 Hz. CMC was about a fifth stronger for monopolar than bipolar and Laplacian derivations. Monopolar derivations resulted in most uniform CMC distributions across the thenar and in tightest cortical source clusters in the left rolandic hand area. CMC was 19–27% stronger for HD-sEMG than for “macroscopic” monopolar or bipolar derivations. EMG rectification reduced the CMC peak by a quarter, resulted in a more uniformly distributed CMC across the thenar, and provided more tightly clustered cortical sources than unrectifed sEMGs. Moreover, it revealed CMC at ∼12 Hz. We conclude that HD-sEMG, especially with monopolar derivation, can facilitate detection of CMC and that individual muscle anatomy cannot explain the high interindividual CMC variability.
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Zhang, Ruihao, Yingping Hong, Huixin Zhang, Lizhi Dang, and Yunze Li. "High-Performance Surface Electromyography Armband Design for Gesture Recognition." Sensors 23, no. 10 (May 21, 2023): 4940. http://dx.doi.org/10.3390/s23104940.
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Wearable surface electromyography (sEMG) signal-acquisition devices have considerable potential for medical applications. Signals obtained from sEMG armbands can be used to identify a person’s intentions using machine learning. However, the performance and recognition capabilities of commercially available sEMG armbands are generally limited. This paper presents the design of a wireless high-performance sEMG armband (hereinafter referred to as the α Armband), which has 16 channels and a 16-bit analog-to-digital converter and can reach 2000 samples per second per channel (adjustable) with a bandwidth of 0.1–20 kHz (adjustable). The α Armband can configure parameters and interact with sEMG data through low-power Bluetooth. We collected sEMG data from the forearms of 30 subjects using the α Armband and extracted three different image samples from the time–frequency domain for training and testing convolutional neural networks. The average recognition accuracy for 10 hand gestures was as high as 98.6%, indicating that the α Armband is highly practical and robust, with excellent development potential.
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Akif Khidirov, Elgun Salahli, Akif Khidirov, Elgun Salahli. "PROGRAM FOR DETERMINING THE INFORMATIVE PARAMETERS OF SURFACE ELECTROMYOGRAPHIC SIGNALS." PIRETC-Proceeding of The International Research Education & Training Centre 27, no. 06 (August 25, 2023): 122–30. http://dx.doi.org/10.36962/piretc27062023-122.
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The article analyzes and calculates informative parameters of surface electromyographic signals (sEMS), which can be used to control biotechnical systems, as well as to diagnose the state of the musculoskeletal system. The analysis parameters in the time and frequency-time domains of the signal are considered. A program has been developed for calculating the informative indicators of the signal in the indicated areas. The program is implemented in the LabVIEW environment. To analyze the sEMG signal in the time domain, using the developed program, such indicators as Integral EMG, Average amplitude change, Wavelength, Simple quadratic integral, Absolute value of the 3rd time moment, and others were calculated; and to describe the signal spectrum by methods of time-frequency analysis, the average frequency of the spectrum (mean power frequency-MPF), the median frequency of the spectrum (median Frequency-MF), root mean square (RMS), power density spectrum (PDS), half width - the width of the spectrum at half maximum amplitude (HW). To test the program, files of real sEMQ signals were used. The calculated parameters of the sEMG analysis in the time and frequency-time domains make it possible to non-invasively and objectively assess the state of the musculoskeletal system. Keywords: Surface electromyographic signals, biotechnical systems, time-frequency analysis, Labvıew software.
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Messaoudi, Noureddine, Samia Belkacem, and Rais El’hadi Bekka. "Simulated Surface Electromyographic (SEMG) Signal Generation and Detection Model." Scientific Bulletin of Electrical Engineering Faculty 23, no. 2 (December 1, 2023): 82–92. http://dx.doi.org/10.2478/sbeef-2023-0024.
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Abstract For didactic purposes, the aim of this work was to improve a simulation model of surface electromyographic (sEMG) signal by taking into consideration the shortcomings of previously developed models. This model started with the simulation of the single fibre action potential (SFAP), then the model of the single motor unit action potential (MUAP), afterwards the imitation of the train of MUAP and finally the modellig of the resultant sEMG signal which is the sum of the MUAPs trains. SFAP simulation was based on: i) the description of the volume conductor model which is composed of four layers (bone, muscle, fat and skin), ii) the description of the electrodes shapes and sizes as well as spatial filters, iii) and the transmebrane current. The proposed model shows its effectiveness in the possibility of carrying out practical work by simulation on the modelling of SFAP, MUAP, MUAPT and the sEMG signal. The most important result of this model is that signal processing tools can be applied to analyze and interpret real-world phenomena such as the effects of physiological, non physiological and sensing system parameters on the shape of the simulated sEMG signal.
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Zeng, Xiong, Ying Dong, and Xiaohao Wang. "Flexible Electrode by Hydrographic Printing for Surface Electromyography Monitoring." Materials 13, no. 10 (May 19, 2020): 2339. http://dx.doi.org/10.3390/ma13102339.
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Surface electromyography (sEMG) monitoring has recently inspired new applications in the field of patient diagnose, rehabilitation therapy, man–machine–interface and prosthesis control. However, conventional wet electrodes for sEMG recording cannot fully satisfy the requirements of these applications because they are based on rigid metals and conductive gels that cause signal quality attenuation, motion artifact and skin allergy. In this study, a novel flexible dry electrode is presented for sEMG monitoring. The electrode is fabricated by screen-printing a silver–eutectic gallium–indium system over a transfer tattoo paper, which is then hydrographically printed on 3D surface or human skin. Peano curve in open-network pattern is adopted to enhance the mechanics of the electrode. Hydrographic printing enables the electrode to attach to skin intimately and conformably, meanwhile assures better mechanical and electrical properties and therefore improves the signal quality and long-term wearability of the electrode. By recording sEMG signal of biceps under three kinds of movement with comparison to conventional wet electrode, the feasibility of the presented flexible dry electrode for sEMG monitoring was proved.
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Nacpil, Edric John Cruz, and Kimihiko Nakano. "Surface Electromyography-Controlled Automobile Steering Assistance." Sensors 20, no. 3 (February 2, 2020): 809. http://dx.doi.org/10.3390/s20030809.
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Disabilities of the upper limb, such as hemiplegia or upper limb amputation, can limit automobile drivers to steering with one healthy arm. For the benefit of these drivers, recent studies have developed prototype interfaces that realized surface electromyography (sEMG)-controlled steering assistance with path-following accuracy that has been validated with driving simulations. In contrast, the current study expands the application of sEMG-controlled steering assistance by validating the Myo armband, a mass-produced sEMG-based interface, with respect to the path-following accuracy of a commercially available automobile. It was hypothesized that one-handed remote steering with the Myo armband would be comparable or superior to the conventional operation of the automobile steering wheel. Although results of low-speed field testing indicate that the Myo armband had lower path-following accuracy than the steering wheel during a 90° turn and wide U-turn at twice the minimum turning radius, the Myo armband had superior path-following accuracy for a narrow U-turn at the minimum turning radius and a 45° turn. Given its overall comparability to the steering wheel, the Myo armband could be feasibly applied in future automobile studies.
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Saraiva, Bruno, Ester Silva, Rodrigo Polaquini Simões, Ana Paula Urdiales Garcia, Fabrício Augusto Menegon, Daniel Iwai Sakabe, Rodrigo Lício Ortolan, Luiz Eduardo Barreto Martins, Lucien Oliveira, and Aparecida Maria Catai. "Heart rate variability and surface electromyography of trained cyclists at different cadences." Motricidade 12, no. 1 (June 23, 2016): 43. http://dx.doi.org/10.6063/motricidade.4221.
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<p class="ResumoAbstract">The heart rate variability (HRV) and surface electromyography (sEMG) are important tools in the evaluation of cardiac autonomic system and neuromuscular parameters, respectively. The aim of the study was to evaluate the behavior of HRV and sEMG of the vastus lateralis in two exercise protocols on a cycle ergometer at 60 and 80 rpm. Eight healthy men cyclists who have trained for at least two years were evaluated. Reduction was observed followed by stabilization of RMSSD and SDNN indices of HRV (p<0.05) along with increases in the amplitude of the sEMG signal (p<0.05) in both protocols. Significant correlations were observed between the responses of HRV and sEMG in the cadence of 60 rpm (RMSSD and sEMG: r = -0.42, p=0.03; SDNN and sEMG: r = -0.45, p=0.01) and 80 rpm (RMSSD and sEMG: r = -0.47, p=0.02; SDNN and sEMG: r = -0.49, p=0.01), yet no difference was observed for these variables between the two protocols. We concluded that the parasympathetic cardiac responses and sEMG are independent of cadences applied at the same power output.</p>
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Li, Yuchang, Hongqing Pan, and Quanjun Song. "ADS1299-Based Array Surface Electromyography Signal Acquisition System." Journal of Physics: Conference Series 2383, no. 1 (December 1, 2022): 012054. http://dx.doi.org/10.1088/1742-6596/2383/1/012054.
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A multi-channel sEMG signal acquisition system based on the analog front-end chip ADS1299 is designed. The whole acquisition system consists of a 2×9 high-density electrode array, ADS1299 multi-channel high-precision A/D conversion chip; A MCU named STM32F103C8, an upper computer, and PC. We carried out electrode array design, The introduction of the function of the ADS1299 chip, and the circuit design of the analog signal acquisition part. The test results show that the acquisition system designed in this paper can ideally collect the sEMG signal of 8 channels on the back of the hand, which proves the effectiveness of this design in extracting weak EMG signals. Therefore, it has reference significance for designing larger-scale sEMG signal acquisition circuits.
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Gamucci, Fiorenza, Marcello Pallante, Sybille Molle, Enrico Merlo, and Andrea Bertuglia. "A Preliminary Study on the Use of HD-sEMG for the Functional Imaging of Equine Superficial Muscle Activation during Dynamic Mobilization Exercises." Animals 12, no. 6 (March 20, 2022): 785. http://dx.doi.org/10.3390/ani12060785.
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Superficial skeletal muscle activation is associated with an electric activity. Bidimensional High-Density Surface Electromyography (HD-sEMG) is a non-invasive technique that uses a grid of equally spaced electrodes applied on the skin surface to detect and portray superficial skeletal muscle activation. The goal of the study was to evaluate the feasibility of HD-sEMG to detect electrical activation of skeletal muscle and its application during rehabilitation exercises in horses. To fulfil this aim, activation of the superficial descending pectoral and external abdominal oblique core muscles were measured using HD-sEMG technology during dynamic mobilization exercises to induce lateral bending and flexion/extension tasks of the trunk. Masseter muscle was instrumented during mastication as a control condition. A 64 surface EMG channel wireless system was used with a single 64 electrode grid or a pair of 32 electrode grids. HD-sEMG provided unique information on the muscular activation onset, duration, and offset, along each motor task, and permitting inferences about the motor control strategy actuated by the central nervous system. Signals were further processed to obtain firing frequencies of few motor-neurons. Estimation of electromyographic amplitude and spectral parameters allowed detecting the onset of muscular fatigue during the motor tasks performed. HD-sEMG allows the assessment of muscular activation in horses performing specific motor tasks, supporting its future application in clinical and research settings.
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Pietropaoli, Davide, Eleonora Ortu, Mario Giannoni, Ruggero Cattaneo, Alessandra Mummolo, and Annalisa Monaco. "Alterations in Surface Electromyography Are Associated with Subjective Masticatory Muscle Pain." Pain Research and Management 2019 (November 22, 2019): 1–9. http://dx.doi.org/10.1155/2019/6256179.
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Background. Tenderness of masseters and temporalis can be considered a relevant tool for diagnosis of myo-type craniofacial pain disorders, but a limit of pain score systems is that they are based on subjective pain perception. Surface electromyography (sEMG) is a noninvasive and reliable tool for recording muscle activity. Therefore, we investigated whether a correlation exists between tenderness on masseters and temporalis, assessed by subjective pain scale, and muscles activity, evaluated by sEMG, in patients with painful temporomandibular disorder (TMD) and concurrent tension-type headache (TTH). Methods. A cross-sectional study on fifty adult volunteer patients with TMD and TTH, who underwent tenderness protocol according to Diagnostic Criteria for TMD (DC/TMD) guidelines, was conducted followed by sEMG recording of temporalis and masseters. Pearson’s correlation was performed to investigate the correlation between muscular activity and subjective pain scores. Results. An overall moderate correlation between muscle tenderness and sEMG values (y = 1 + 1.2 · x; r2 = 0.62; p<0.0001), particularly in the temporalis, was observed. Segregation of data occurred according to tenderness and sEMG values. At the highest pain score, the mean sEMG absolute value was higher at the temporalis than the masseters. Conclusions. Our study provides evidence that subjective pain perception can be objectively quantified at a magnitude proportional to pain severity. At greater tenderness scores, higher sEMG activity at the level of temporalis could help discriminate clinically prevalent TTH versus prevalent TMD. sEMG confirms to be an accurate tool to reliably objectify the subjective perception of pain. When combined with clinical evaluation and patients’ symptoms, sEMG increases diagnostic sensitivity in the field of myo-type craniofacial pain disorders. This trial is registered with NCT02789085.
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Rosenthal, Ronald. "Techniques to Manage ECG Artifacts When Working With Surface EMG." Biofeedback 48, no. 4 (December 1, 2020): 80–84. http://dx.doi.org/10.5298/1081-5937-48.04.02.
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Surface electromyographic (SEMG) amplitude signals can often contain rhythmic spikes due to cardiogenic electrical activity. The author discusses the impact of this activity on SEMG biofeedback training and techniques to reduce the problems caused by cardiogenic electrical activity. In particular, changing the low frequency cutoff of the digital filter settings to reduce cardiogenic electrical activity is recommended as a procedure to improve the fidelity of SEMG amplitude signals.
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Szyszka-Sommerfeld, Liliana, Magdalena Sycińska-Dziarnowska, Agata Budzyńska, and Krzysztof Woźniak. "Accuracy of Surface Electromyography in the Diagnosis of Pain-Related Temporomandibular Disorders in Children with Awake Bruxism." Journal of Clinical Medicine 11, no. 5 (February 28, 2022): 1323. http://dx.doi.org/10.3390/jcm11051323.
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The study assessed masticatory muscle electromyographic (EMG) activity in both children diagnosed with pain-related temporomandibular disorders (TMD-P) and awake bruxism (AB) and in children without TMD, as well as the diagnostic value of surface electromyography (sEMG) in diagnosing TMD-P in subjects with AB. After evaluation based on the Axis I of the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD), 30 children diagnosed with myofascial pain were included in the myofascial pain group and 30 children without TMD diagnosis comprised the control group (mean age of 9.49 ± 1.34 years). The activity of the anterior temporal (TA) and masseter (MM) muscle was assessed bilaterally using a DAB-Bluetooth device (zebris Medical GmBH, Germany) at rest and during maximum voluntary clenching (MVC). The receiver operating characteristic (ROC) curve was used to determine the accuracy, sensitivity, and specificity of the normalized sEMG data. Statistically significant intergroup differences were observed in TA and MM muscle EMG activity at rest and during MVC. Moderate degree of sEMG accuracy in discriminating between TMD-P and non-TMD children was observed for TAmean, left MM, and MMmean EMG muscle activity at rest. sEMG can be a useful tool in assessing myofascial TMD pain in patients with AB.
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Peng, Xiangdong, Xiao Zhou, Huaqiang Zhu, Zejun Ke, and Congcheng Pan. "MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition." PLOS ONE 17, no. 11 (November 7, 2022): e0276436. http://dx.doi.org/10.1371/journal.pone.0276436.
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In the field of surface electromyography (sEMG) gesture recognition, how to improve recognition accuracy has been a research hotspot. The rapid development of deep learning provides a new solution to this problem. At present, the main applications of deep learning for sEMG gesture feature extraction are based on convolutional neural network (CNN) structures to capture spatial morphological information of the multichannel sEMG or based on long short-term memory network (LSTM) to extract time-dependent information of the single-channel sEMG. However, there are few methods to comprehensively consider the distribution area of the sEMG signal acquisition electrode sensor and the arrangement of the sEMG signal morphological features and electrode spatial features. In this paper, a novel multi-stream feature fusion network (MSFF-Net) model is proposed for sEMG gesture recognition. The model adopts a divide-and-conquer strategy to learn the relationship between different muscle regions and specific gestures. Firstly, a multi-stream convolutional neural network (Multi-stream CNN) and a convolutional block attention module integrated with a resblock (ResCBAM) are used to extract multi-dimensional spatial features from signal morphology, electrode space, and feature map space. Then the learned multi-view depth features are fused by a view aggregation network consisting of an early fusion network and a late fusion network. The results of all subjects and gesture movement validation experiments in the sEMG signal acquired from 12 sensors provided by NinaPro’s DB2 and DB4 sub-databases show that the proposed model in this paper has better performance in terms of gesture recognition accuracy compared with the existing models.
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Nacpil, Edric, Zheng Wang, Rencheng Zheng, Tsutomu Kaizuka, and Kimihiko Nakano. "Design and Evaluation of a Surface Electromyography-Controlled Steering Assistance Interface." Sensors 19, no. 6 (March 15, 2019): 1308. http://dx.doi.org/10.3390/s19061308.
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Millions of drivers could experience shoulder muscle overload when rapidly rotating steering wheels and reduced steering ability at increased steering wheel angles. In order to address these issues for drivers with disability, surface electromyography (sEMG) sensors measuring biceps brachii muscle activity were incorporated into a steering assistance system for remote steering wheel rotation. The path-following accuracy of the sEMG interface with respect to a game steering wheel was evaluated through driving simulator trials. Human participants executed U-turns with differing radii of curvature. For a radius of curvature equal to the minimum vehicle turning radius of 3.6 m, the sEMG interface had significantly greater accuracy than the game steering wheel, with intertrial median lateral errors of 0.5 m and 1.2 m, respectively. For a U-turn with a radius of 7.2 m, the sEMG interface and game steering wheel were comparable in accuracy, with respective intertrial median lateral errors of 1.6 m and 1.4 m. The findings of this study could be utilized to realize accurate sEMG-controlled automobile steering for persons with disability.
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Xie, Jing Jin, and Lei Zuo. "Design of SEMG Recognition System." Advanced Materials Research 403-408 (November 2011): 4194–98. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4194.
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Surface electromyography (sEMG) is recorded from the surface of skeleton muscle by electrodes, it is the bioelectricity discharged by neuromuscular activities. This paper designed a data acquisition platform of sEMG, which contains hardware module and software module. The hardware contains electrodes, microcomputer, power and filters. The software contains data reading and storage, signal filter, timer and graphics user interface. It shows the system is workable with stable performance.
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Neblett, Randy. "Surface Electromyographic (SEMG) Biofeedback for Chronic Low Back Pain." Healthcare 4, no. 2 (May 17, 2016): 27. http://dx.doi.org/10.3390/healthcare4020027.
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Kaczmarek, Piotr, Tomasz Mańkowski, and Jakub Tomczyński. "putEMG—A Surface Electromyography Hand Gesture Recognition Dataset." Sensors 19, no. 16 (August 14, 2019): 3548. http://dx.doi.org/10.3390/s19163548.
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In this paper, we present a putEMG dataset intended for the evaluation of hand gesture recognition methods based on sEMG signal. The dataset was acquired for 44 able-bodied subjects and include 8 gestures (3 full hand gestures, 4 pinches and idle). It consists of uninterrupted recordings of 24 sEMG channels from the subject’s forearm, RGB video stream and depth camera images used for hand motion tracking. Moreover, exemplary processing scripts are also published. The putEMG dataset is available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. An accuracy of 90% was achieved for SVM classifier utilising RMS feature and for LDA classifier using Hudgin’s and Du’s feature sets. Analysis of performance for particular gestures showed that LDA/Du combination has significantly higher accuracy for full hand gestures, while SVM/RMS performs better for pinch gestures. The presented dataset can be used as a benchmark for various classification methods, the evaluation of electrode localisation concepts, or the development of classification methods invariant to user-specific features or electrode displacement.
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Szyszka-Sommerfeld, Liliana, Mariusz Lipski, and Krzysztof Woźniak. "Surface Electromyography as a Method for Diagnosing Muscle Function in Patients with Congenital Maxillofacial Abnormalities." Journal of Healthcare Engineering 2020 (September 22, 2020): 1–6. http://dx.doi.org/10.1155/2020/8846920.
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Electromyography (EMG) is the most objective and reliable method available for imaging muscle function and efficiency, which is done by identifying their electrical potentials. In global surface electromyography (sEMG), surface electrodes are located on the surface of the skin, and it detects superimposed motor unit action potentials from many muscle fibers. sEMG is widely used in orthodontics and maxillofacial orthopaedics to diagnose and treat temporomandibular disorders (TMD) in patients, assess stomatognathic system dysfunctions in patients with malocclusions, and monitor orthodontic therapies. Information regarding muscle sEMG activity in subjects with congenital maxillofacial abnormalities is limited. For this reason, the aim of this review is to discuss the usefulness of surface electromyography as a method for diagnosing muscle function in patients with congenital malformations of the maxillofacial region. Original papers on this subject, published in English between 1995 until 2020, are located in the MEDLINE/PubMed database.
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Dai, Qingfeng, Yongkang Wong, Mohan Kankanhali, Xiangdong Li, and Weidong Geng. "Improved Network and Training Scheme for Cross-Trial Surface Electromyography (sEMG)-Based Gesture Recognition." Bioengineering 10, no. 9 (September 20, 2023): 1101. http://dx.doi.org/10.3390/bioengineering10091101.
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To enhance the performance of surface electromyography (sEMG)-based gesture recognition, we propose a novel network-agnostic two-stage training scheme, called sEMGPoseMIM, that produces trial-invariant representations to be aligned with corresponding hand movements via cross-modal knowledge distillation. In the first stage, an sEMG encoder is trained via cross-trial mutual information maximization using the sEMG sequences sampled from the same time step but different trials in a contrastive learning manner. In the second stage, the learned sEMG encoder is fine-tuned with the supervision of gesture and hand movements in a knowledge-distillation manner. In addition, we propose a novel network called sEMGXCM as the sEMG encoder. Comprehensive experiments on seven sparse multichannel sEMG databases are conducted to demonstrate the effectiveness of the training scheme sEMGPoseMIM and the network sEMGXCM, which achieves an average improvement of +1.3% on the sparse multichannel sEMG databases compared to the existing methods. Furthermore, the comparison between training sEMGXCM and other existing networks from scratch shows that sEMGXCM outperforms the others by an average of +1.5%.
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Zhao, Juan, Jinhua She, Dianhong Wang, and Feng Wang. "Extreme Gradient Boosting for Surface Electromyography Classification on Time-Domain Features." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 5 (September 20, 2022): 722–30. http://dx.doi.org/10.20965/jaciii.2022.p0722.
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Surface electromyography (sEMG) signals play an essential role in disease diagnosis and rehabilitation. This study applied a powerful machine learning algorithm called extreme gradient boosting (XGBoost) to classify sEMG signals acquired from muscles around the knee for distinguishing patients with knee osteoarthritis (KOA) from healthy subjects. First, to improve data quality, we preprocessed the data via interpolation and normalization. Next, to ensure the description integrity of model input, we extracted nine time-domain features based on the statistical characteristics of sEMG signals over time. Finally, we classified the samples using XGBoost and cross-validation (CV) and compared the results to those produced by the support vector machine (SVM) and the deep neural network (DNN). Experimental results illustrate that the presented method effectively improves classification performance. Moreover, compared with the SVM and the DNN, XGBoost has higher accuracy and better classification performance, which indicates its advantages in the classification of patients with KOA based on sEMG signals.
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Caffrey, Thomas R., and Robert Clasby. "Surface Electromyography-Assisted Ergonomic Analysis in a Newspaper Printing Plant: A Case Study." Biofeedback 38, no. 4 (January 1, 2010): 155–57. http://dx.doi.org/10.5298/1081-5937-38.4.06.
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Abstract This case study reports on the use of surface electromyography (SEMG) evaluation in a work environment, including production, to show a relationship between muscle dysfunction and specific job tasks and their injury potential. The results show that SEMG can help identify discordant muscle activity as part of an ergonomic evaluation. Such an evaluation leads to improvement in muscle function through SEMG-guided worker/workplace retraining.
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Cao, Tianao, Dan Liu, Qisong Wang, Ou Bai, and Jinwei Sun. "Surface Electromyography-Based Action Recognition and Manipulator Control." Applied Sciences 10, no. 17 (August 22, 2020): 5823. http://dx.doi.org/10.3390/app10175823.
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To improve the quality of lives of disabled people, the application of intelligent prosthesis was presented and investigated. In particular, surface Electromyography (sEMG) signals succeeded in controlling the manipulator in human–machine interface, due to the fact that EMG activity belongs to one of the most widely utilized biosignals and can reflect the straightforward motion intention of humans. However, the accuracy of real-time action recognition is usually low and there is usually obvious delay in a controlling manipulator, as a result of which the task of tracking human movement precisely, cannot be guaranteed. Therefore, this study proposes a method of action recognition and manipulator control. We built a multifunctional sEMG detection and action recognition system that integrated all discrete components. A biopotential measurement analog-to-digital converter with a high signal–noise rate (SNR) was chosen to ensure the high quality of the acquired sEMG signals. The acquired data were divided into sliding windows for processing in a shorter time. Mean Absolute Value (MAV), Waveform Length (WL), and Root Mean Square (RMS) were finally extracted and we found that compared to the Genetic-Algorithm-based Support Vector Machine (GA–SVM), the back propagation (BP) neural network performed better in joint action classification. The results showed that the average accuracy of judging the 5 actions (fist clenching, hand opening, wrist flexion, wrist extension, and calling me) was up to 93.2% and the response time was within 200 ms, which achieved a simultaneous control of the manipulator. Our work took into account the action recognition accuracy and real-time performance, and realized the sEMG-based manipulator control eventually, which made it easier for people with arm disabilities to communicate better with the outside world.
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Kim, Gyu Hui, Jung Hyeon Park, Tae Kyung Kim, Eun Ju Lee, Su Eun Jung, Jong Cheol Seo, Cheol Hong Kim, Yoo Min Choi, and Hyun Min Yoon. "Correlation Between Accompanying Symptoms of Facial Nerve Palsy, Clinical Assessment Scales and Surface Electromyography." Journal of Acupuncture Research 39, no. 4 (November 30, 2022): 297–303. http://dx.doi.org/10.13045/jar.2022.00220.
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Background: This retrospective study aimed to determine whether there were correlations between the number and type of accompanying symptoms of peripheral facial nerve palsy, and surface electromyography (SEMG) and clinical assessment scales to help diagnosis.Methods: There were 30, cases of peripheral facial nerve palsy at Visit 1 to the Korean Medicine Hospital, Dong-eui University, 22 cases at Visit 2 and 10 cases at Visit 3. The study period was from July 19, 2021 to November 31, 2021. Symptoms were evaluated three times (with two-week intervals which began 7 days from onset) using SEMG, clinical assessment scales and accompanying symptoms. In this study, the House-Brackmann grading system (HBGS), and the Yanagihara’s unweighted grading system (Y-score) clinical assessment scales were used. The Pearson or Spearman correlation was used for statistical analysis.Results: On Visit 1, the number of accompanying symptoms of peripheral facial nerve palsy had no significant correlation with other measures. On Visits 1-3, the HBGS score had a significant negative correlation with the Y-score. On Visit 2, most of the mean values measured had significant correlations with each other although not between SEMG-Z and SEMG-O that Z means a zygomaticus muscle and O means a orbicularis oris muscle. On Visit 3, the number of accompanying symptoms significantly correlated with the clinical assessment scales. The HBGS score, Y-score, and SEMG measurements (except SEMG-Z) had significant correlations with each other. A significant positive correlation between SEMG-Z and SEMG-T was noted.Conclusion: We predict accompanying symptoms can be used to diagnose the peripheral facial nerve palsy including both clinical assessment scales and SEMG measurements at 2-5 weeks after onset.
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Krishnamani, Divya Bharathi, P. A. Karthick, and Ramakrishnan Swaminathan. "VARIATION OF INSTANTANEOUS SPECTRAL CENTROID ACROSS BANDS OF SURFACE ELECTROMYOGRAPHIC SIGNALS." Biomedical Sciences Instrumentation 57, no. 2 (April 1, 2021): 356–60. http://dx.doi.org/10.34107/yhpn9422.04356.
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Surface electromyography (sEMG) is a technique which noninvasively acquires the electrical activity of muscles and is widely used for muscle fatigue assessment. This study attempts to characterize the dynamic muscle fatiguing contractions with frequency bands of sEMG signals and a geometric feature namely the instantaneous spectral centroid (ISC). The sEMG signals are acquired from biceps brachii muscle of fifty-eight healthy volunteers. The frequency components of the signals are divided into low frequency band (10-45Hz), medium frequency band (55-95Hz) and high frequency band (95-400Hz). The signals associated with these bands are subjected to a Hilbert transform and analytical shape representation is obtained in the complex plane. The ISC feature is extracted from the resultant shape of the three frequency bands. The results show that this feature can differentiate the muscle nonfatigue and fatigue conditions (p<0.05). It is found the values of ISC is lower in fatigue conditions irrespective of frequency bands. It is also observed that the coefficient of variation of ISC in the low frequency band is less and it demonstrates the ability of handling inter-subject variations. Therefore, the proposed geometric feature from the low frequency band of sEMG signals could be considered for detecting muscle fatigue in various neuromuscular conditions.
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Weng, Mingyuan. "Enhancing Surface Electromyography (sEMG) Signal Processing through a Four-bit Absolute Value Comparator." Highlights in Science, Engineering and Technology 71 (November 28, 2023): 374–81. http://dx.doi.org/10.54097/hset.v71i.13439.
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This paper delves into the realm of Surface Electromyography (sEMG) signal processing, presenting a comprehensive exploration of its theoretical underpinnings and the application of a four-bit absolute value comparator. The journey commences with an introduction to the subject matter, followed by an in-depth analysis of the theoretical basis of sEMG signals, encompassing their definition and waveform characteristics, as well as the processing flow. The focal point of this study is the utilization of a four-bit absolute value comparator in enhancing sEMG signal processing. Moving forward, the paper delves into the intricacies of logic circuit design, elucidating the architecture of both adder and comparator circuits pivotal in this context. Circuit optimization strategies are subsequently unveiled, addressing critical path considerations, gate sizing, and VDD optimization to bolster efficiency. In summation, this research advances our understanding of sEMG signal processing and introduces a novel four-bit absolute value comparator, which holds promise in elevating the precision and reliability of sEMG data analysis.
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Zhang, Zhen, Changxin He, and Kuo Yang. "A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network." Sensors 20, no. 14 (July 17, 2020): 3994. http://dx.doi.org/10.3390/s20143994.
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Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach such as recurrent neural network (RNN) has provided a choice to automatically learn features from raw data. This paper presents a novel hand gesture prediction method by using an RNN model to learn from raw sEMG data and predict gestures. The sEMG signals of 21 short-term hand gestures of 13 subjects were recorded with a Myo armband, which is a non-intrusive, low cost, commercial portable device. At the start of the gesture, the trained model outputs an instantaneous prediction for the sEMG data. Experimental results showed that the more time steps of data that were known, the higher instantaneous prediction accuracy the proposed model gave. The predicted accuracy reached about 89.6% when the data of 40-time steps (200 ms) were used to predict hand gesture. This means that the gesture could be predicted with a delay of 200 ms after the hand starts to perform the gesture, instead of waiting for the end of the gesture.
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Ning, Yong, Yuming Zhao, Akbarjon Juraboev, Ping Tan, Jin Ding, and Jinbao He. "Multichannel Surface EMG Decomposition Based on Measurement Correlation and LMMSE." Journal of Healthcare Engineering 2018 (June 28, 2018): 1–12. http://dx.doi.org/10.1155/2018/2347589.
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A method based on measurement correlation (MC) and linear minimum mean square error (LMMSE) for multichannel surface electromyography (sEMG) signal decomposition was developed in this study. This MC-LMMSE method gradually and iteratively increases the correlation between an optimized vector and a reconstructed matrix that is correlated with the measurement matrix. The performance of the proposed MC-LMMSE method was evaluated with both simulated and experimental sEMG signals. Simulation results show that the MC-LMMSE method can successfully reconstruct up to 53 innervation pulse trains with a true positive rate greater than 95%. The performance of the MC-LMMSE method was also evaluated using experimental sEMG signals collected with a 64-channel electrode array from the first dorsal interosseous muscles of three subjects at different contraction levels. A maximum of 16 motor units were successfully extracted from these multichannel experimental sEMG signals. The performance of the MC-LMMSE method was further evaluated with multichannel experimental sEMG data by using the “two sources” method. The large population of common MUs extracted from the two independent subgroups of sEMG signals demonstrates the reliability of the MC-LMMSE method in multichannel sEMG decomposition.
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Guia Rosa, Igor Da, Marco Antonio Cavalcanti Garcia, and Marcio Nogueira De Souza. "Investigation of probability density functions in modeling sample distribution of surface electromyographic (sEMG) signals." Archives of Control Sciences 23, no. 4 (December 1, 2013): 381–93. http://dx.doi.org/10.2478/acsc-2013-0023.
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Abstract The surface electromyography signal (sEMG) has been typically modeled as a Gaussian random process. However, some authors have reported that the probability density functions (pdfs) associated with the sample distribution of sEMG signal exhibits a more peaked shape than one could expected for a Gaussian pdf. This work aimed to reinvestigate the profile of the sEMG pdfs during five different load levels of isometric contractions of biceps brachii muscle, and compared the adequacy of four different pdfs (Gaussian, Logistic, Cauchy, and Laplacian) in describing the sample distribution of such signal. Experimental pdfs were estimated for each subject and load condition. The comparison between experimental pdfs obtained from sEMG data of forty volunteers and four theoretical pdfs was performed by fitting these functions to its experimental counterpart, and using a mean absolute errors in the assessment of the best fit. On average, the Logistic pdf seemed to be the best one to describe the sample distribution of sEMG signal, although the probabilistic results, considering binomial trials, were significant for both Gaussian and Logistic pdfs.
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St. George, L., T. J. P. Spoormakers, S. H. Roy, S. J. Hobbs, H. M. Clayton, J. Richards, and F. M. Serra Bragança. "Reliability of surface electromyographic (sEMG) measures of equine axial and appendicular muscles during overground trot." PLOS ONE 18, no. 7 (July 14, 2023): e0288664. http://dx.doi.org/10.1371/journal.pone.0288664.
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The reliability of surface electromyography (sEMG) has not been adequately demonstrated in the equine literature and is an essential consideration as a methodology for application in clinical gait analysis. This observational study investigated within-session, intra-subject (stride-to-stride) and inter-subject reliability, and between-session reliability of normalised sEMG activity profiles, from triceps brachii (triceps), latissimus dorsi (latissimus), longissimus dorsi (longissimus), biceps femoris (biceps), superficial gluteal (gluteal) and semitendinosus muscles in n = 8 clinically non-lame horses during in-hand trot. sEMG sensors were bilaterally located on muscles to collect data during two test sessions (session 1 and 2) with a minimum 24-hour interval. Raw sEMG signals from ten trot strides per horse and session were DC-offset removed, high-pass filtered (40 Hz), full-wave rectified, and low-pass filtered (25 Hz). Signals were normalised to peak amplitude and percent stride before calculating intra- and inter-subject ensemble average sEMG profiles across strides for each muscle and session. sEMG profiles were assessed using waveform similarity statistics: the coefficient of variation (CV) to assess intra- and inter-subject reliability and the adjusted coefficient of multiple correlation (CMC) to evaluate between-session reliability. Across muscles, CV data revealed that intra-horse sEMG profiles within- and between-sessions were comparatively more reliable than inter-horse profiles. Bilateral gluteal, semitendinosus, triceps and longissimus (at T14 and L1) and right biceps showed excellent between-session reliability with group-averaged CMCs > 0.90 (range 0.90–0.97). Bilateral latissimus and left biceps showed good between-session reliability with group-averaged CMCs > 0.75 (range 0.78–0.88). sEMG profiles can reliably describe fundamental muscle activity patterns for selected equine muscles within a test session for individual horses (intra-subject). However, these profiles are more variable across horses (inter-subject) and between sessions (between-session reliability), suggesting that it is reasonable to use sEMG to objectively monitor the intra-individual activity of these muscles across multiple gait evaluation sessions at in-hand trot.
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Vojtech, Jennifer M., Michael D. Chan, Bhawna Shiwani, Serge H. Roy, James T. Heaton, Geoffrey S. Meltzner, Paola Contessa, Gianluca De Luca, Rupal Patel, and Joshua C. Kline. "Surface Electromyography–Based Recognition, Synthesis, and Perception of Prosodic Subvocal Speech." Journal of Speech, Language, and Hearing Research 64, no. 6S (June 18, 2021): 2134–53. http://dx.doi.org/10.1044/2021_jslhr-20-00257.
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Purpose This study aimed to evaluate a novel communication system designed to translate surface electromyographic (sEMG) signals from articulatory muscles into speech using a personalized, digital voice. The system was evaluated for word recognition, prosodic classification, and listener perception of synthesized speech. Method sEMG signals were recorded from the face and neck as speakers with ( n = 4) and without ( n = 4) laryngectomy subvocally recited (silently mouthed) a speech corpus comprising 750 phrases (150 phrases with variable phrase-level stress). Corpus tokens were then translated into speech via personalized voice synthesis ( n = 8 synthetic voices) and compared against phrases produced by each speaker when using their typical mode of communication ( n = 4 natural voices, n = 4 electrolaryngeal [EL] voices). Naïve listeners ( n = 12) evaluated synthetic, natural, and EL speech for acceptability and intelligibility in a visual sort-and-rate task, as well as phrasal stress discriminability via a classification mechanism. Results Recorded sEMG signals were processed to translate sEMG muscle activity into lexical content and categorize variations in phrase-level stress, achieving a mean accuracy of 96.3% ( SD = 3.10%) and 91.2% ( SD = 4.46%), respectively. Synthetic speech was significantly higher in acceptability and intelligibility than EL speech, also leading to greater phrasal stress classification accuracy, whereas natural speech was rated as the most acceptable and intelligible, with the greatest phrasal stress classification accuracy. Conclusion This proof-of-concept study establishes the feasibility of using subvocal sEMG-based alternative communication not only for lexical recognition but also for prosodic communication in healthy individuals, as well as those living with vocal impairments and residual articulatory function. Supplemental Material https://doi.org/10.23641/asha.14558481
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Adeel, Muhammad, Hung-Chou Chen, Bor-Shing Lin, Chien-Hung Lai, Chun-Wei Wu, Jiunn-Horng Kang, Jian-Chiun Liou, and Chih-Wei Peng. "Oxygen Consumption (VO2) and Surface Electromyography (sEMG) during Moderate-Strength Training Exercises." International Journal of Environmental Research and Public Health 19, no. 4 (February 16, 2022): 2233. http://dx.doi.org/10.3390/ijerph19042233.
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Oxygen consumption (VO2) during strength training can be predicted through surface electromyography (sEMG) of local muscles. This research aimed to determine relations between VO2 and sEMG of upper and lower body muscles to predict VO2 from sEMG during moderate-intensity strength training exercises. Of the 12 participants recruited, 11 were divided into two groups: untrained (n = 5; with no training experience) and trained (n = 6; with 2 months of training experience). On different days, each individual completed six training sessions. Each participant performed training sessions consisting of three types of dumbbell exercises: shoulder press, deadlift, and squat, while wearing a mask for indirect calorimetric measurements of VO2 using the Cortex Metalyzer 3B. sEMG measurements of the bilateral middle deltoid, lumbar erector spinae, quadriceps (rectus femoris), and hamstring (biceps femoris) muscles were recorded. The VO2 was predicted from sEMG root mean square (RMS) values of the investigated muscles during the exercise period using generalized estimating equation (GEE) modeling. The predicted models for the three types of exercises for the untrained vs. trained groups were shoulder press [QIC = 102, * p = 0.000 vs. QIC = 82, * p = 0.000], deadlift [QIC = 172, * p = 0.000 vs. QIC = 320, * p = 0.026], and squat [QIC = 76, * p = 0.000 vs. QIC = 348, * p = 0.001], respectively. It was observed that untrained vs. trained groups predicted GEE models [quasi-likelihood under an independence model criterion (QIC) = 368, p = 0.330 vs. QIC = 837, p = 0.058], respectively. The study obtained significant VO2 prediction models during shoulder press, deadlift, and squat exercises using the right and left middle deltoid, right and left lumbar erector spinae, left rectus femoris, and right and left biceps femoris sEMG RMS for the untrained and trained groups during moderate-intensity strength training exercises.
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Dorgham, Osama, Ibrahim Al-Mherat, Jawdat Al-Shaer, Sulieman Bani-Ahmad, and Stephen Laycock. "Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network." Future Internet 11, no. 1 (January 21, 2019): 25. http://dx.doi.org/10.3390/fi11010025.
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Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.
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Chen, Lin, Jianting Fu, Yuheng Wu, Haochen Li, and Bin Zheng. "Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals." Sensors 20, no. 3 (January 26, 2020): 672. http://dx.doi.org/10.3390/s20030672.
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By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.
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Wang, You, Hengyang Wang, Huiyan Li, Asif Ullah, Ming Zhang, Han Gao, Ruifen Hu, and Guang Li. "Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography." Sensors 21, no. 15 (July 23, 2021): 4994. http://dx.doi.org/10.3390/s21154994.
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Based on surface electromyography (sEMG), a novel recognition method to distinguish six types of human primary taste sensations was developed, and the recognition accuracy was 74.46%. The sEMG signals were acquired under the stimuli of no taste substance, distilled vinegar, white granulated sugar, instant coffee powder, refined salt, and Ajinomoto. Then, signals were preprocessed with the following steps: sample augments, removal of trend items, high-pass filter, and adaptive power frequency notch. Signals were classified with random forest and the classifier gave a five-fold cross-validation accuracy of 74.46%, which manifested the feasibility of the recognition task. To further improve the model performance, we explored the impact of feature dimension, electrode distribution, and subject diversity. Accordingly, we provided an optimized feature combination that reduced the number of feature types from 21 to 4, a preferable selection of electrode positions that reduced the number of channels from 6 to 4, and an analysis of the relation between subject diversity and model performance. This study provides guidance for further research on taste sensation recognition with sEMG.
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Wang, Junhong, Lipeng Wang, Xugang Xi, Seyed M. Miran, and Anke Xue. "Estimation and Correlation Analysis of Lower Limb Joint Angles Based on Surface Electromyography." Electronics 9, no. 4 (March 26, 2020): 556. http://dx.doi.org/10.3390/electronics9040556.
Full textAbstract:
Many people lose their motor function because of spinal cord injury or stroke. This work studies the patient’s continuous movement intention of joint angles based on surface electromyography (sEMG), which will be used for rehabilitation. In this study, we introduced a new sEMG feature extraction method based on wavelet packet decomposition, built a prediction model based on the extreme learning machine (ELM) and analyzed the correlation between sEMG signals and joint angles based on the detrended cross-correlation analysis. Twelve individuals participated in rehabilitation tasks, to test the performance of the proposed method. Five channels of sEMG signals were recorded, and denoised by the empirical mode decomposition. The prediction accuracy of the wavelet packet feature-based ELM prediction model was found to be 96.23% ± 2.36%. The experimental results clearly indicate that the wavelet packet feature and ELM is a better combination to build a prediction model.
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Yang, Chun, Jinyi Long, and Hao Wang. "Performance Comparison of Classification Methods for Surface EMG-Based Human-Machine Interface." International Journal of Grid and High Performance Computing 7, no. 4 (October 2015): 47–56. http://dx.doi.org/10.4018/ijghpc.2015100104.
Full textAbstract:
Reliable control of assistive devices through surface electromyography (sEMG) based human-machine interfaces (HMIs) requires accurate classification of multi-channel sEMG. The design of effective pattern classification methods is one of the main challenges for sEMG-based HMIs. In this paper, the authors compared comprehensively the performance of different linear and nonlinear classifiers for the pattern classification of sEMG with respect to three pairs of upper-limb motions (i.e., hand close vs. hand open, wrist flexion vs. wrist extension, and forearm pronation vs. forearm supination). A feature selection approach based on information gain was also performed to reduce the muscle channels. Overall, the results showed that the linear classifiers produce slightly better classification performance, with or without the muscle channel selection.
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Dorosz, Tomasz, Aleksandra Mańko, and Michał Ginszt. "Use of Surface Electromyography to Evaluate Effects of Therapeutic Methods on Masticatory Muscle Activity in Patients with Temporomandibular Disorders: A Narrative Review." Journal of Clinical Medicine 13, no. 3 (February 5, 2024): 920. http://dx.doi.org/10.3390/jcm13030920.
Full textAbstract:
The presented narrative review aims to present the impact of therapeutic methods on the masticatory muscle activity measured using surface electromyography (sEMG) in patients with temporomandibular disorders (TMDs). Original interventional studies with baseline data for diagnosed TMD groups with full-text articles in English published in scientific journals in the last ten years were included in the evaluation process. The following narrative review considered only clinical, controlled, and randomized studies. Articles that included the following parameters were qualified for this review: adult participants, diagnosis of temporomandibular disorder, the presence of a musculoskeletal dysfunction, no other severe comorbidities, use of therapeutic interventions, and sEMG measurement before and after the intervention. Ten papers were accepted and analyzed for the final evaluation in the presented review. Several studies using surface electromyographic examination prove the effectiveness of various therapies to normalize the bioelectrical activity of the masticatory muscles, either reduction during rest or increase during a functional task in patients diagnosed with temporomandibular disorders. This narrative review shows the influence of manual and physical treatments on electromyographic masticatory muscle activity, including soft tissue mobilization, transcutaneous electrical nerve stimulation, low-level laser therapy, and moist heat therapy. Changes in masticatory muscle activity coincided with changes in TMD-associated pain and range of mandibular mobility.
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Ding, Shuai, Liang Wang, Zhan Peng Sun, Wei Jin Gao, and Shou Long Fang. "Movement Identification Based on Transient sEMG for Control of Prosthesis." Advanced Materials Research 971-973 (June 2014): 1651–54. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1651.
Full textAbstract:
Researches on surface electromyography (sEMG) for upper-limb prosthesis control have been going on for several years. Most published studies on prosthesis usually use the steady-state sEMG or the transient sEMG for identification. However, the transient sEMG is less stable than steady-state sEMG. The nonstationarity in transient sEMG greatly affects the performance of myoelectric control. In this paper, we propose a method based on sparse representation to capture the characteristics of transient sEMG to identify movements. Experiment results show the proposed method extracts the variations in transient sEMG activity from different movements effectively. The proposed feature achieves a satisfactory classification rate, which outperforms the other features.
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Miró, Francisco, Alfonso M. Galisteo, Juan L. Garrido-Castro, and Joaquín Vivo. "Surface Electromyography of the Longissimus and Gluteus Medius Muscles in Greyhounds Walking and Trotting on Ground Flat, Up, and Downhill." Animals 10, no. 6 (June 3, 2020): 968. http://dx.doi.org/10.3390/ani10060968.
Full textAbstract:
In the field of canine rehabilitation, knowledge of muscle function in the therapeutic exercises prescribed is needed by physical therapists and veterinary surgeons. To gain insight into the function of longissimus dorsi (LD) and gluteus medius (GM) muscles in dogs, five Greyhounds performing leash walking and trotting on the ground flat, up (+7%), and downhill (−7%) were studied by surface electromyography, and the mean and maximum activity was compared. For the same incline, the surface electromyography (sEMG) of LD was higher (p < 0.05) at the trot than at the walk. In LD muscle, trotting uphill showed significantly higher maximum activity than any other exercise. A change of +7% incline or −7% decline affected (increased or decreased, respectively) the mean sEMG of the LD and GM muscles of dogs walking or trotting on the ground. When combined, the influence of gait and incline on electromyographic activity was analyzed, and walking at certain inclines showed no difference with trotting at certain inclines. Walking and trotting up and downhill added separate therapeutic value to flat motion. The results of the present study might contribute to a better understanding of the function of LD and GM muscles in dogs, this being especially useful for the field of canine rehabilitation.