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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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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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Chen, Jiangcheng, Sheng Bi, George Zhang, and Guangzhong Cao. "High-Density Surface EMG-Based Gesture Recognition Using a 3D Convolutional Neural Network." Sensors 20, no. 4 (February 21, 2020): 1201. http://dx.doi.org/10.3390/s20041201.
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High-density surface electromyography (HD-sEMG) and deep learning technology are becoming increasingly used in gesture recognition. Based on electrode grid data, information can be extracted in the form of images that are generated with instant values of multi-channel sEMG signals. In previous studies, image-based, two-dimensional convolutional neural networks (2D CNNs) have been applied in order to recognize patterns in the electrical activity of muscles from an instantaneous image. However, 2D CNNs with 2D kernels are unable to handle a sequence of images that carry information concerning how the instantaneous image evolves with time. This paper presents a 3D CNN with 3D kernels to capture both spatial and temporal structures from sequential sEMG images and investigates its performance on HD-sEMG-based gesture recognition in comparison to the 2D CNN. Extensive experiments were carried out on two benchmark datasets (i.e., CapgMyo DB-a and CSL-HDEMG). The results show that, where the same network architecture is used, 3D CNN can achieve a better performance than 2D CNN, especially for CSL-HDEMG, which contains the dynamic part of finger movement. For CapgMyo DB-a, the accuracy of 3D CNN was 1% higher than 2D CNN when the recognition window length was equal to 40 ms, and was 1.5% higher when equal to 150 ms. For CSL-HDEMG, the accuracies of 3D CNN were 15.3% and 18.6% higher than 2D CNN when the window length was equal to 40 ms and 150 ms, respectively. Furthermore, 3D CNN achieves a competitive performance in comparison to the baseline methods.
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van Dijk, J. P., D. Kusters, N. van Alfen, M. J. Zwarts, D. F. Stegeman, and G. Drost. "FC7.2 Using high-density surface electromyography (HD-sEMG) in detecting neuromuscular disorders in children." Clinical Neurophysiology 117 (September 2006): 1–2. http://dx.doi.org/10.1016/j.clinph.2006.06.025.
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Favretto, M. A., S. Cossul, F. R. Andreis, L. R. Nakamura, M. F. Ronsoni, S. Tesfaye, D. Selvarajah, and J. L. B. Marques. "Alterations of tibialis anterior muscle activation pattern in subjects with type 2 diabetes and diabetic peripheral neuropathy." Biomedical Physics & Engineering Express 8, no. 2 (January 5, 2022): 025001. http://dx.doi.org/10.1088/2057-1976/ac455b.
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Abstract Diabetic peripheral neuropathy (DPN) is associated with loss of motor units (MUs), which can cause changes in the activation pattern of muscle fibres. This study investigated the pattern of muscle activation using high-density surface electromyography (HD-sEMG) signals from subjects with type 2 diabetes mellitus (T2DM) and DPN. Thirty-five adults participated in the study: 12 healthy subjects (HV), 12 patients with T2DM without DPN (No-DPN) and 11 patients with T2DM with DPN (DPN). HD-sEMG signals were recorded in the tibialis anterior muscle during an isometric contraction of ankle dorsiflexion at 50% of the maximum voluntary isometric contraction (MVIC) during 30-s. The calculated HD-sEMG signals parameters were the normalised root mean square (RMS), normalised median frequency (MDF), coefficient of variation (CoV) and modified entropy (ME). The RMS increased significantly (p = 0.001) with time only for the DPN group, while the MDF decreased significantly (p < 0.01) with time for the three groups. Moreover, the ME was significantly lower (p = 0.005), and CoV was significantly higher (p = 0.003) for the DPN group than the HV group. Using HD-sEMG, we have demonstrated a reduction in the number of MU recruited by individuals with DPN. This study provides proof of concept for the clinical utility of this technique for identifying neuromuscular impairment caused by DPN.
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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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Jaber, Hanadi, Mofeed rashid, and Luigi Fortuna. "Interactive Real-Time Control System for The Artificial Hand." Iraqi Journal for Electrical and Electronic Engineering 16, no. 1 (May 11, 2020): 1–10. http://dx.doi.org/10.37917/ijeee.16.1.8.
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In recent years, the number of researches in the field of artificial limbs has increased significantly in order to improve the performance of the use of these limbs by amputees. During this period, High-Density surface Electromyography (HD-sEMG) signals have been employed for hand gesture identification, in which the performance of the classification process can be improved by using robust spatial features extracted from HD-sEMG signals. In this paper, several algorithms of spatial feature extraction have been proposed to increase the accuracy of the SVM classifier, while the histogram oriented gradient (HOG) has been used to achieve this mission. So, several feature sets have been extracted from HD-sEMG signals such as; features extracted based on HOG denoted by (H); features have been generated by combine intensity feature with H features denoted as (HI); features have been generated by combine average intensity with H features denoted as (AIH). The proposed system has been simulated by MATLAB to calculate the accuracy of the classification process, in addition, the proposed system is practically validated in order to show the ability to use this system by amputees. The results show the high accuracy of the classifier in real-time which leads to an increase in the possibility of using this system as an artificial hand.
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Lapatki, B. G., J. P. van Dijk, I. E. Jonas, M. J. Zwarts, and D. F. Stegeman. "A thin, flexible multielectrode grid for high-density surface EMG." Journal of Applied Physiology 96, no. 1 (January 2004): 327–36. http://dx.doi.org/10.1152/japplphysiol.00521.2003.
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Although the value of high-density surface electromyography (sEMG) has already been proven in fundamental research and for specific diagnostic questions, there is as yet no broad clinical application. This is partly due to limitations of construction principles and application techniques of conventional electrode array systems. We developed a thin, highly flexible, two-dimensional multielectrode sEMG grid, which is manufactured by using flexprint techniques. The material used as electrode carrier (Polyimid, 50 μm thick) allows grids to be cut out in any required shape or size. One universal grid version can therefore be used for many applications, thereby reducing costs. The reusable electrode grid is attached to the skin by using specially prepared double-sided adhesive tape, which allows the selective application of conductive cream only directly below the detection surfaces. To explore the practical possibilities, this technique was applied in single motor unit analysis of the facial musculature. The high mechanical flexibility allowed the electrode grid to follow the skin surface even in areas with very uneven contours, resulting in good electrical connections in the whole recording area. The silverchloride surfaces of the electrodes and their low electrode-to-skin impedances guaranteed high baseline stability and a low signal noise level. The electrode-to-skin attachment proved to withstand saliva and great tensile forces due to mimic contractions. The inexpensive, universally adaptable and minimally obstructive sensor allows the principal advantages of high-density sEMG to be extended to all skeletal muscles accessible from the skin surface and may lay the foundation for more broad clinical application of this noninvasive, two-dimensional sEMG technique.
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Zhang, Yingwei, Yiqiang Chen, Hanchao Yu, Xiaodong Yang, Ruizhe Sun, and Bixiao Zeng. "A Feature Adaptive Learning Method for High-Density sEMG-Based Gesture Recognition." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 1 (March 19, 2021): 1–26. http://dx.doi.org/10.1145/3448114.
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Surface electromyography (sEMG) array based gesture recognition, which is widely-used, could provide natural surfaces for human-computer interaction. Currently, most existing gesture recognition methods with sEMG array only work with the fixed and pre-defined electrodes configuration. However, changes in the number of electrodes (i.e., increment or decrement) is common in real scenarios due to the variability of physiological electrodes. In this paper, we study this challenging problem and propose a random forest based ensemble learning method, namely feature incremental and decremental ensemble learning (FIDE). FIDE is able to support continuous changes in the number of electrodes by dynamically maintaining the matrix sketches of every sEMG electrode and spatial structure of sEMG array. To evaluate the performance of FIDE, we conduct extensive experiments on three benchmark datasets, including NinaPro, CSL-hdemg, and CapgMyo. Experimental results demonstrate that FIDE outperforms other state-of-the-art methods and has the potential to adapt to the evolution of electrodes in the changing environments. Moreover, based on FIDE, we implement a multi clients/server collaboration system, namely McS, to support feature adaption in real-world environment. By collecting sEMG using two clients (smartphone and personal computer) and adaptively recognizing gestures in the cloud server, FIDE significantly improves the gesture recognition accuracy in electrode increment and decrement circumstances.
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Gat, Liraz, Aaron Gerston, Liu Shikun, Lilah Inzelberg, and Yael Hanein. "Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions." PLOS ONE 17, no. 2 (February 22, 2022): e0262286. http://dx.doi.org/10.1371/journal.pone.0262286.
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Computer vision (CV) is widely used in the investigation of facial expressions. Applications range from psychological evaluation to neurology, to name just two examples. CV for identifying facial expressions may suffer from several shortcomings: CV provides indirect information about muscle activation, it is insensitive to activations that do not involve visible deformations, such as jaw clenching. Moreover, it relies on high-resolution and unobstructed visuals. High density surface electromyography (sEMG) recordings with soft electrode array is an alternative approach which provides direct information about muscle activation, even from freely behaving humans. In this investigation, we compare CV and sEMG analysis of facial muscle activation. We used independent component analysis (ICA) and multiple linear regression (MLR) to quantify the similarity and disparity between the two approaches for posed muscle activations. The comparison reveals similarity in event detection, but discrepancies and inconsistencies in source identification. Specifically, the correspondence between sEMG and action unit (AU)-based analyses, the most widely used basis of CV muscle activation prediction, appears to vary between participants and sessions. We also show a comparison between AU and sEMG data of spontaneous smiles, highlighting the differences between the two approaches. The data presented in this paper suggests that the use of AU-based analysis should consider its limited ability to reliably compare between different sessions and individuals and highlight the advantages of high-resolution sEMG for facial expression analysis.
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Campanini, Isabella, Andrea Merlo, Catherine Disselhorst-Klug, Luca Mesin, Silvia Muceli, and Roberto Merletti. "Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors." Sensors 22, no. 11 (May 30, 2022): 4150. http://dx.doi.org/10.3390/s22114150.
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Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.
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Lanza, Marcel, Vicki Gray, Alice Ryan, Will Perez, and Odessa Addison. "Role of Intramuscular Fat and Lean Muscle in Surface Electromyography Amplitude of the Gluteus Medius in Older Adults." Innovation in Aging 4, Supplement_1 (December 1, 2020): 127. http://dx.doi.org/10.1093/geroni/igaa057.417.
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Abstract Surface electromyography (sEMG) is frequently used to assess muscle activation in older individuals. Subcutaneous fat is one well-known factor that influences sEMG amplitude. The amount of intramuscular fat (IMAT) may negatively impact the muscles ability to produce force with aging, while high density lean tissue (HDL; fat free muscle) has an opposite effect. However, influence of IMAT or HDL on sEMG amplitude remains unclear. Thus, the aim was to investigate the influence of IMAT and HDL on sEMG amplitude of the gluteus medius (GM) muscle during a maximal voluntary isometric contraction (MVIC) in older adults. Twelve older adults (7 females; age: 71±3 y; BMI= 29±4 Kg/m2; X ± SD) underwent a CT scan to determine IMAT and HDL cross-sectional area in the GM. IMAT and HDL were normalized as a percentage of the total muscle area. Maximal hip abduction MVIC was measured at 30□ hip abduction in standing, while sEMG was recorded from the GM muscle. Spearman correlations showed a positive association between GM HDL and sEMG amplitude (r = 0.692, P = 0.013) and negative between GM IMAT and sEMG amplitude (r = -0.683, P = 0.014). This is the first study to demonstrate the amount of IMAT may limit the ability to activate the hip abductor muscle. Given that muscle activation is a determinant of strength, interventions to lower levels of IMAT and increase levels of lean muscle may be important to slowing decreases in strength with aging.
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Maeda, Noriaki, Makoto Komiya, Yuichi Nishikawa, Masanori Morikawa, Shogo Tsutsumi, Tsubasa Tashiro, Kazuki Fukui, Hiroaki Kimura, and Yukio Urabe. "Effect of Acute Static Stretching on the Activation Patterns Using High-Density Surface Electromyography of the Gastrocnemius Muscle during Ramp-Up Task." Sensors 21, no. 14 (July 15, 2021): 4841. http://dx.doi.org/10.3390/s21144841.
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This study aimed to evaluate motor unit recruitment during submaximal voluntary ramp contraction in the medial head of the gastrocnemius muscle (MG) by high-density spatial electromyography (SEMG) before and after static stretching (SS) in healthy young adults. SS for gastrocnemius was performed in 15 healthy participants for 2 min. Normalized peak torque by bodyweight of the plantar flexor, muscle activity at peak torque, and muscle activation patterns during ramp-up task were evaluated before and after SS. Motor unit recruitment during the submaximal voluntary contraction of the MG was measured using SEMG when performing submaximal ramp contractions during isometric ankle plantar flexion from 30 to 80% of the maximum voluntary contraction (MVC). To evaluate the changes in the potential distribution of SEMG, the root mean square (RMS), modified entropy, and coefficient of variation (CV) were calculated from the dense surface EMG data when 10% of the MVC force was applied. Muscle activation patterns during the 30 to 80% of MVC submaximal voluntary contraction tasks were significantly changed from 50 to 70% of MVC after SS when compared to before. The variations in motor unit recruitment after SS indicate diverse motor unit recruitments and inhomogeneous muscle activities, which may adversely affect the performance of sports activities.
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Hossen, A., G. Deuschl, S. Groppa, U. Heute, and M. Muthuraman. "Discrimination of physiological tremor from pathological tremor using accelerometer and surface EMG signals." Technology and Health Care 28, no. 5 (September 18, 2020): 461–76. http://dx.doi.org/10.3233/thc-191947.
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BACKGROUND AND OBJECTIVE: Although careful clinical examination and medical history are the most important steps towards a diagnostic separation between different tremors, the electro-physiological analysis of the tremor using accelerometry and electromyography (EMG) of the affected limbs are promising tools. METHODS: A soft-decision wavelet-based decomposition technique is applied with 8 decomposition stages to estimate the power spectral density of accelerometer and surface EMG signals (sEMG) sampled at 800 Hz. A discrimination factor between physiological tremor (PH) and pathological tremor, namely, essential tremor (ET) and the tremor caused by Parkinson’s disease (PD), is obtained by summing the power entropy in band 6 (B6: 7.8125–9.375 Hz) and band 11 (B11: 15.625–17.1875 Hz). RESULTS: A discrimination accuracy of 93.87% is obtained between the PH group and the ET & PD group using a voting between three results obtained from the accelerometer signal and two sEMG signals. CONCLUSION: Biomedical signal processing techniques based on high resolution wavelet spectral analysis of accelerometer and sEMG signals are implemented to efficiently perform classification between physiological tremor and pathological tremor.
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Ibrahim, Ayad Assad, Ikhlas Mahmoud Farhan, and Mohammed Ehasn Safi. "A nonlinearities inverse distance weighting spatial interpolation approach applied to the surface electromyography signal." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (April 1, 2022): 1530. http://dx.doi.org/10.11591/ijece.v12i2.pp1530-1539.
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Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode grid from different types of subjects, sex, age, and type of muscles have been studied when muscles are under regular tension activity. A goodness of fit measure (R2) is used to evaluate the proposed technique. The interpolated signals are compared with the actual signals; the Goodness of fit measure's value is almost 99%, with a processing time of 100msec. The resulting technique is shown to be of high accuracy and matching of spatial interpolated signals to actual signals compared with IDW and Kriging techniques.
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Park, So Young, and Chan Hyuk Park. "Diagnosis of Muscle Fatigue Using Surface Electromyography and Analysis of Associated Factors in Type 2 Diabetic Patients with Neuropathy: A Preliminary Study." International Journal of Environmental Research and Public Health 18, no. 18 (September 13, 2021): 9635. http://dx.doi.org/10.3390/ijerph18189635.
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Diabetic neuropathy (DN) is a major complication associated with diabetes mellitus (DM) and results in fatigue. We investigated whether type 2 diabetic patients with or without neuropathy experienced muscle fatigue and determined the most influencing factor on muscle fatigue. Overall, 15 out of 25 patients with type 2 DM were diagnosed with DN using a nerve conduction study in the upper and lower extremities, and the composite score (CS) was calculated. We obtained the duration of DM and body mass index (BMI) from subjects, and they underwent a series of laboratory tests including HbA1c, fasting plasma glucose, triglycerides, and high- and low-density lipoprotein. To qualify muscle fatigue, this study used surface electromyography (sEMG). Anode and cathode electrodes were attached to the medial gastrocnemius. After 100% isometric maximal voluntary contracture of plantarflexion, the root mean square, median frequency (MDF), and mean power frequency (MNF) were obtained. We showed a correlation among laboratory results, duration of DM, BMI, CS, and parameters of muscle fatigue. The duration of DM was related to fatigue of the muscle and CS (p < 0.05). However, CS was not related to fatigue. The MDF and MNF of muscle parameters were positively correlated with HbA1c and fasting plasma glucose (p < 0.05). In conclusion, we suggest that the duration of DM and glycemic control play important roles in muscle fatigue in patients with DN. Additionally, sEMG is useful for diagnosing muscle fatigue in patients with DN.
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Gao, Jianxin, Cheng Han, Wenying Huang, Jianjun Gao, and Liaoliang Nie. "Experimental study on the sEMG of "joint angle effect" of human muscle strength—Taking biceps brachii as an example." E3S Web of Conferences 145 (2020): 01021. http://dx.doi.org/10.1051/e3sconf/202014501021.
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In the experiment, the author used wave plus wireless surface electromyography system (SEMs + 3-axis acceleration sensor) made in Italy and wave wireless EMG software system, high-definition high-speed camera and human joint angle measuring instrument. Taking human biceps brachii as an example, the static and dynamic isometric contraction of biceps brachii was completed surface electromyography. In the experiment, the surface electromyography of biceps brachii was measured at 30°, 60°, 90°, 120°, 150°, 180° and the surface electromyography of biceps brachii was measured at the same time when the biceps brachii was not loaded or when the biceps brachii was loaded. Secondly, the surface electromyography of biceps brachii was measured at the same time when the biceps brachii completed the whole process of flexion and extension of the elbow (centripetal and centrifugal). Finally, the paper combined with HD The effect of joint angle on the contraction of biceps brachii muscle was analyzed by camera technique. The results show that the static contraction force of biceps brachii is different when the elbow joint is at different angles; in addition, when the dynamic contraction, the contraction force of biceps brachii is inversely proportional to the angle of elbow joint.
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Dai, Chenyun, and Xiaogang Hu. "Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings." International Journal of Neural Systems 29, no. 01 (January 10, 2019): 1850025. http://dx.doi.org/10.1142/s0129065718500259.
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The human hand is capable of producing versatile yet precise movements largely owing to the complex neuromuscular systems that control our finger movement. This study seeks to quantify the spatial activation patterns of the forearm flexor muscles during individualized finger flexions. High-density (HD) surface electromyogram (sEMG) signals of forearm flexor muscles were obtained, and individual motor units were decomposed from the sEMG. Both macro-level spatial patterns of EMG activity and micro-level motor unit distributions were used to systematically characterize the forearm flexor activation patterns. Different features capturing the spatial patterns were extracted, and the unique patterns of forearm flexor activation were then quantified using pattern recognition approaches. We found that the forearm flexor spatial activation during the ring finger flexion was mostly distinct from other fingers, whereas the activation patterns of the middle finger were least distinguishable. However, all the different activation patterns can still be classified in high accuracy (94–100%) using pattern recognition. Our findings indicate that the partial overlapping of neural activation can limit accurate identification of specific finger movement based on limited recordings and sEMG features, and that HD sEMG recordings capturing detailed spatial activation patterns at both macro- and micro-levels are needed.
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Hajian, Gelareh, Ali Etemad, and Evelyn Morin. "Automated Channel Selection in High-Density sEMG for Improved Force Estimation." Sensors 20, no. 17 (August 27, 2020): 4858. http://dx.doi.org/10.3390/s20174858.
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Accurate and real-time estimation of force from surface electromyogram (EMG) signals enables a variety of applications. We developed and validated new approaches for selecting subsets of high-density (HD) EMG channels for improved and lower-dimensionality force estimation. First, a large dataset was recorded from a number of participants performing isometric contractions in different postures, while simultaneously recording HD-EMG channels and ground-truth force. The EMG signals were acquired from three linear surface electrode arrays, each with eight monopolar channels, and were placed on the long head and short head of the biceps brachii and brachioradialis. After data collection and pre-processing, fast orthogonal search (FOS) was employed for force estimation. To select a subset of channels, principal component analysis (PCA) in the frequency domain and a novel index called the power-correlation ratio (PCR), which maximizes the spectral power while minimizing similarity to other channels, were used. These approaches were compared to channel selection using time-domain PCA. We selected one, two, and three channels per muscle from the original seven differential channels to reduce the redundancy and correlation in the dataset. In the best case, we achieved an approximate improvement of 30% for force estimation while reducing the dimensionality by 57% for a subset of three channels.
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Robinault, Lucien, Aleš Holobar, Sylvain Crémoux, Usman Rashid, Imran Khan Niazi, Kelly Holt, Jimmy Lauber, and Heidi Haavik. "The Effects of Spinal Manipulation on Motor Unit Behavior." Brain Sciences 11, no. 1 (January 14, 2021): 105. http://dx.doi.org/10.3390/brainsci11010105.
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Over recent years, a growing body of research has highlighted the neural plastic effects of spinal manipulation on the central nervous system. Recently, it has been shown that spinal manipulation improved outcomes, such as maximum voluntary force and limb joint position sense, reflecting improved sensorimotor integration and processing. This study aimed to further evaluate how spinal manipulation can alter neuromuscular activity. High density electromyography (HD sEMG) signals from the tibialis anterior were recorded and decomposed in order to study motor unit changes in 14 subjects following spinal manipulation or a passive movement control session in a crossover study design. Participants were asked to produce ankle dorsiflexion at two force levels, 5% and 10% of maximum voluntary contraction (MVC), following two different patterns of force production (“ramp” and “ramp and maintain”). A significant decrease in the conduction velocity (p = 0.01) was observed during the “ramp and maintain” condition at 5% MVC after spinal manipulation. A decrease in conduction velocity suggests that spinal manipulation alters motor unit recruitment patterns with an increased recruitment of lower threshold, lower twitch torque motor units.
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Duan, Haiqiang, Chenyun Dai, and Wei Chen. "The Evaluation of Classifier Performance during Fitting Wrist and Finger Movement Task Based on Forearm HD-sEMG." Mathematical Problems in Engineering 2022 (March 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/9594521.
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The transmission of human body movement signals to other devices through wearable smart bracelets has attracted increasing attention in the field of human-machine interfaces. However, owing to the limited data collection range of wearable bracelets, it is necessary to study the relationship between the superposition of the wrist and fingers and their cooperative motions to simplify the data collection system of such devices. Multichannel high-density surface electromyogram (HD-sEMG) signals exhibit high spatial resolutions, and they can help improve the accuracy of the multichannel fitting. In this study, we quantified the HD-sEMG forearm spatial activation features of 256 channels of hand movement and performed a linear fitting of the data obtained for finger and wrist movements in order to verify the linear superposition relationship between the cooperative and independent movements of the wrist and fingers. This study aims to classify and predict the results of the fitting and measured fingers and wrist cooperative actions using four commonly adopted classifiers and evaluate the performance of the classifiers in gesture fitting. The results indicated that linear discriminant analysis affords the highest classification performance, whereas the random forest method achieved the worst performance. This study can serve as a guide for gesture signal simplification in the future.
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Cavalcanti, Jéssica D., Guilherme Augusto F. Fregonezi, Antonio J. Sarmento, Thiago Bezerra, Lucien P. Gualdi, Francesca Pennati, Andrea Aliverti, and Vanessa R. Resqueti. "Electrical activity and fatigue of respiratory and locomotor muscles in obstructive respiratory diseases during field walking test." PLOS ONE 17, no. 4 (April 1, 2022): e0266365. http://dx.doi.org/10.1371/journal.pone.0266365.
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Introduction In subjects with obstructive respiratory diseases the increased work of breathing during exercise can trigger greater recruitment and fatigue of respiratory muscles. Associated with these changes, lower limb muscle dysfunctions, further contribute to exercise limitations. We aimed to assess electrical activity and fatigue of two respiratory and one locomotor muscle during Incremental Shuttle Walking Test (ISWT) in individuals with obstructive respiratory diseases and compare with healthy. Methods This is a case-control study. Seventeen individuals with asthma (asthma group) and fifteen with chronic obstructive pulmonary disease (COPD group) were matched with healthy individuals (asthma and COPD control groups). Surface electromyographic (sEMG) activity of sternocleidomastoid (SCM), scalene (ESC), and rectus femoris (RF) were recorded during ISWT. sEMG activity was analyzed in time and frequency domains at baseline and during the test (33%, 66%, and 100% of ISWT total time) to obtain, respectively, signal amplitude and power spectrum density (EMG median frequency [MF], high- and low-frequency bands, and high/low [H/L] ratio). Results Asthma group walked a shorter distance than controls (p = 0.0007). sEMG amplitudes of SCM, ESC, and RF of asthma and COPD groups were higher at 33% and 66% of ISWT compared with controls groups (all p<0.05). SCM and ESC of COPD group remained higher until 100% of the test. MF of ESC and RF decreased in asthma group (p = 0.016 and p < 0.0001, respectively) versus controls, whereas MF of SCM (p < 0.0001) decreased in COPD group compared with controls. H/L ratio of RF decreased (p = 0.002) in COPD group versus controls. Conclusion Reduced performance is accompanied by increased electromyographic activity of SCM and ESC and activation of RF in individuals with obstructive respiratory diseases during ISWT. These are susceptible to be more pronounced respiratory and peripheral muscle fatigue than healthy subjects during exercise.
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Tong, Michael Chi-Fai. "Understanding nasopharyngeal carcinoma." Impact 2018, no. 3 (June 15, 2018): 14–15. http://dx.doi.org/10.21820/23987073.2018.3.14.
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An international collaborative project, based at the Chinese University of Hong Kong, seeks to drastically improve the prognosis and quality of life for nasopharyngeal cancer patients. Hong Kong has experienced a rise in the incidence of nasopharyngeal carcinoma (NPC) in an increasingly ageing population. Swallowing disorders and neuropathy are common occurrences in survivors, as a result of continued exposure to chemotherapy. Most of the techniques developed in studying this pathological phenomenon are invasive and impractical and complicate the establishment of clinical correlations between current treatment courses and physiological events. There is therefore a pressing need for better treatment strategies and early diagnostic methods that allow subclinical identification. Professor Michael Chi-Fai Tong, from the Chinese University of Hong Kong's Department of Otorhinolaryngology, is investigating the pathophysiologic mechanisms that lead to methods with novel approaches.During the studies, Tong revamped common evaluation techniques for swallowing motor capacity in order to perfect the assessment of muscle function in irradiated nasopharyngeal cancer patients. During the studies, Tong revamped common evaluation techniques for swallowing motor capacity in order to perfect the assessment of muscle function in irradiated nasopharyngeal cancer patients. While FEES and VFSS were the most explored techniques, Tong also focused on developing a better understanding on results obtained from HD-EMG performed on dysphagia subjects. The chosen parameters were related to SEMG signals that, when brought together provided the opportunity to map out muscular activities involved in the act of swallowing. From all the techniques, high-density electromyography brought forward the highest number of clinical correlations to the physiology behind dysphagia, namely by analysing aspiration rates, penetration, oral and pharyngeal stasis and premature spillage as measures for early diagnosis, early intervention and monitoring treatment progress. After applying the techniques to patients participating in the trial, Tong concluded that dysphagia indeed remained a major clinical problem for surviving patients and a significant number suffered from adverse effects that compromised their quality of life. Nearly 30 per cent of the studied population experienced subsequent health burdens as well as higher medical expenses. 'Values obtained from sEMG reflected actual changes in the geniohyoid muscle during contraction, whilst ultrasound imaging allowed direct visualisation and measurement of the muscle,' Tong explains. 'Both are useful techniques in providing supplementary information regarding muscle function, and provide quick tools to monitor treatment progress, but more effort is needed to prevent or delay the occurrence of dysphagia and its related complications.' Novel methods of swallowing training were tested among survivors. Tong compared the outcomes of conventional training with transcutaneous electrical stimulation (TES) and found that TES had a superior outcome compared to conventional treatments. Despite having obtained positive results, validation of these methods is dependent on the sample size that the team used to execute the studies, as conventional swallowing assessments are subjective and require quantitative parameters to reach an objective evaluation. However, Tong is positive that those results will be attained, especially with the help of the multidisciplinary team gathered through this consortium, from laryngologists, to speech therapists and academic research staff. The methods developed are still in need of optimisation, but with this collaborative endeavour, Tong and his team will surely come up with a new strategy, providing a deeper understanding of neck muscle defects in surviving NPC patients for future applications.
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Imrani, Loubna, Sofiane Boudaoud, Clément Lahaye, Caroline Moreau, Myriam Ghezal, Safa Ben Manaa, Mohamed Doulazmi, Jérémy Laforêt, Frédéric Marin, and Kiyoka Kinugawa. "High-density surface electromyography as biomarker of muscle aging." Journals of Gerontology: Series A, July 25, 2022. http://dx.doi.org/10.1093/gerona/glac143.
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Abstract Sarcopenia is a muscle disease with adverse changes that increase throughout lifetime, but with different chronological scale between individuals. Addressing “early muscle aging" is becoming a critical issue for prevention. Through the CHRONOS study, we demonstrated the ability of the high-density surface electromyography (HD-sEMG), a non-invasive, wireless, portable technology, to detect both healthy muscle aging and accelerated muscle aging related to sedentary lifestyle, one of the risk factors of sarcopenia. The HD-sEMG signals were analyzed in 91 healthy young, middle-aged and old subjects (25-75yrs) distributed according to their physical activity status (82 active and 9 sedentary; IPAQ) and compared with current methods for muscle evaluation including muscle mass (dual-energy X-ray absorptiometry, ultrasonography), handgrip strength, physical performance. The HD-sEMG signals were recorded from the rectus femoris during sit-to-stand trials, and two indexes were analyzed: Muscular Contraction Intensity and Muscle Contraction Dynamics. The clinical parameters did not differ significantly across the aging and physical activity levels. Inversely, the HD-sEMG indexes were correlated to age, and were different significantly through the age categories of the 82 active subjects. They were significantly different between sedentary subjects aged 45-54 years and active ones at the same age. The HD-sEMG indexes of sedentary subjects were not significantly different from those of older active subjects (≥55years). The Muscle thicknesses evaluated using ultrasonography were different significantly between the 5 age decades, but did not show a significant difference with physical activity. The HD-sEMG technique can assess muscle aging and physical inactivity-related “early aging” outperforming clinical and DXA parameters.
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Rojas-Martínez, Mónica, Leidy Yanet Serna, Mislav Jordanic, Hamid Reza Marateb, Roberto Merletti, and Miguel Ángel Mañanas. "High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans." Scientific Data 7, no. 1 (November 16, 2020). http://dx.doi.org/10.1038/s41597-020-00717-6.
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AbstractThis paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from five upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classification methods. About 6% of the total number of signals were identified as “bad” channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identification of effort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identification among other uses.
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Souza de Oliveira, Daniela, Andrea Casolo, Thomas G. Balshaw, Sumiaki Maeo, Marcel Bahia Lanza, Neil R. W. Martin, Nicola Maffulli, et al. "Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units." Journal of Neural Engineering, July 19, 2022. http://dx.doi.org/10.1088/1741-2552/ac823d.
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Abstract Objective. High-density surface electromyography (HD-sEMG) allows the reliable identification of individual motor unit (MU) action potentials. Despite the accuracy in decomposition, there is a large variability in the number of identified MUs across individuals and exerted forces. Here we present a systematic investigation of the anatomical and neural factors that determine this variability. Approach. We investigated factors of influence on HD-sEMG decomposition, such as synchronization of MU discharges, distribution of MU territories, muscle-electrode distance (MED - subcutaneous adipose tissue thickness), maximum anatomical cross-sectional area (ACSAmax), and fiber CSA. For this purpose, we recorded HD-sEMG signals, ultrasound and, magnetic resonance images, and took a muscle biopsy from the biceps brachii muscle from 30 male participants drawn from two groups to ensure variability within the factors – untrained-controls (UT=14) and strength-trained individuals (ST=16). Participants performed isometric ramp contractions with elbow flexors (at 15, 35, 50 and 70% maximum voluntary torque - MVT). We assessed the correlation between the number of accurately detected MUs by HD-sEMG decomposition and each measured parameter, for each target force level. Multiple regression analysis was then applied. Main results. ST subjects showed lower MED (UT=5.1±1.4 mm; ST=3.8±0.8 mm) and a greater number of identified motor units (UT:21.3±10.2 vs ST:29.2±11.8 MUs/subject across all force levels). The entire cohort showed a negative correlation between MED and the number of identified MUs at low forces (r= -0.6, p=0.002 at 15%MVT). Moreover, the number of identified MUs was positively correlated to the distribution of MU territories (r=0.56, p=0.01) and ACSAmax (r=0.48, p=0.03) at 15%MVT. By accounting for all anatomical parameters, we were able to partly predict the number of decomposed MUs at low but not at high forces. Significance. Our results confirmed the influence of subcutaneous tissue on the quality of HD-sEMG signals and demonstrated that MU spatial distribution and ACSAmax are also relevant parameters of influence for current decomposition algorithms.
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Zhang, Xu, Xinhui Li, Xiao Tang, Xun Chen, Xiang Chen, and Ping Zhou. "Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury." Journal of NeuroEngineering and Rehabilitation 17, no. 1 (December 2020). http://dx.doi.org/10.1186/s12984-020-00786-z.
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Abstract Background Spatial filtering of multi-channel signals is considered to be an effective pre-processing approach for improving signal-to-noise ratio. The use of spatial filtering for preprocessing high-density (HD) surface electromyogram (sEMG) helps to extract critical spatial information, but its application to non-invasive examination of neuromuscular changes have not been well investigated. Methods Aimed at evaluating how spatial filtering can facilitate examination of muscle paralysis, three different spatial filtering methods are presented using principle component analysis (PCA) algorithm, non-negative matrix factorization (NMF) algorithm, and both combination, respectively. Their performance was evaluated in terms of diagnostic power, through HD-sEMG clustering index (CI) analysis of neuromuscular changes in paralyzed muscles following spinal cord injury (SCI). Results The experimental results showed that: (1) The CI analysis of conventional single-channel sEMG can reveal complex neuromuscular changes in paralyzed muscles following SCI, and its diagnostic power has been confirmed to be characterized by the variance of Z scores; (2) the diagnostic power was highly dependent on the location of sEMG recording channel. Directly averaging the CI diagnostic indicators over channels just reached a medium level of the diagnostic power; (3) the use of either PCA-based or NMF-based filtering method yielded a greater diagnostic power, and their combination could even enhance the diagnostic power significantly. Conclusions This study not only presents an essential preprocessing approach for improving diagnostic power of HD-sEMG, but also helps to develop a standard sEMG preprocessing pipeline, thus promoting its widespread application.
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Nishikawa, Yuichi, Kohei Watanabe, Takanori Chihara, Jiro Sakamoto, Toshihiko Komatsuzaki, Kenji Kawano, Akira Kobayashi, et al. "Influence of forward head posture on muscle activation pattern of the trapezius pars descendens muscle in young adults." Scientific Reports 12, no. 1 (November 14, 2022). http://dx.doi.org/10.1038/s41598-022-24095-8.
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AbstractForward head posture (FHP) is a serious problem causing head and neck disability, but the characteristics of muscle activity during long-term postural maintenance are unclear. This study aimed to investigate a comparison of electromyography (EMG) activation properties and subjective fatigue between young adults with and without habitual FHP. In this study, we examined the changes in the spatial and temporal distribution patterns of muscle activity using high-density surface EMG (HD-SEMG) in addition to mean frequency, a conventional measure of muscle fatigue. Nineteen male participants were included in the study (FHP group (n = 9; age = 22.3 ± 1.5 years) and normal group (n = 10; age = 22.5 ± 1.4 years)). Participants held three head positions (e.g., forward, backward, and neutral positions) for a total of 30 min each, and the EMG activity of the trapezius pars descendens muscle during posture maintenance was measured by HD-SEMG. The root mean square (RMS), the modified entropy, and the correlation coefficient were calculated. Additionally, the visual analogue scale (VAS) was evaluated to assess subjective fatigue. The RMS, VAS, modified entropy, and correlation coefficients were significantly higher in the FHP group than in the normal group (p < 0.001). With increasing postural maintenance time, the modified entropy and correlation coefficient values significantly decreased, and the mean frequency and VAS values significantly increased (p < 0.001). Furthermore, the forward position had significantly higher RMS, correlation coefficient, modified entropy, and VAS values than in the neutral position (p < 0.001). The HD-SEMG potential distribution patterns in the FHP group showed less heterogeneity and greater muscle activity in the entire muscle and subjective fatigue than those in the normal group. Excess muscle activity even in the neutral/comfortable position in the FHP group could potentially be a mechanism of neuromuscular conditions in this population.
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Cui, Han, Weizheng Zhong, Zhuoxin Yang, Xuemei Cao, Shuangyan Dai, Xingxian Huang, Liyu Hu, Kai Lan, Guanglin Li, and Haibo Yu. "Comparison of Facial Muscle Activation Patterns Between Healthy and Bell’s Palsy Subjects Using High-Density Surface Electromyography." Frontiers in Human Neuroscience 14 (January 12, 2021). http://dx.doi.org/10.3389/fnhum.2020.618985.
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Facial muscle activities are essential for the appearance and communication of human beings. Therefore, exploring the activation patterns of facial muscles can help understand facial neuromuscular disorders such as Bell’s palsy. Given the irregular shape of the facial muscles as well as their different locations, it should be difficult to detect the activities of whole facial muscles with a few electrodes. In this study, a high-density surface electromyogram (HD sEMG) system with 90 electrodes was used to record EMG signals of facial muscles in both healthy and Bell’s palsy subjects when they did different facial movements. The electrodes were arranged in rectangular arrays covering the forehead and cheek regions of the face. The muscle activation patterns were shown on maps, which were constructed from the Root Mean Square (RMS) values of all the 90-channel EMG recordings. The experimental results showed that the activation patterns of facial muscles were distinct during doing different facial movements and the activated muscle regions could be clearly observed. Moreover, two features of the activation patterns, 2D correlation coefficient (corr2) and Centre of Gravity (CG) were extracted to quantify the spatial symmetry and the location of activated muscle regions respectively. Furthermore, the deviation of activated muscle regions on the paralyzed side of a face compared to the healthy side was quantified by calculating the distance between two sides of CGs. The results revealed that corr2 of the activated facial muscle region (classified into forehead region and cheek region) in Bell’s palsy subjects was significantly (p < 0.05) lower than that in healthy subjects, while CG distance of activated facial region in Bell’s palsy subjects was significantly (p < 0.05) higher than that in healthy subjects. The correlation between corr2 of these regions and Bell’s palsy [assessed by the Facial Nerve Grading Scale (FNGS) 2.0] was also significant (p < 0.05) in Bell’s palsy subjects. The spatial information on activated muscle regions may be useful in the diagnosis and treatment of Bell’s palsy in the future.
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Balbinot, Gustavo, Guijin Li, Matheus Joner Wiest, Maureen Pakosh, Julio Cesar Furlan, Sukhvinder Kalsi-Ryan, and Jose Zariffa. "Properties of the surface electromyogram following traumatic spinal cord injury: a scoping review." Journal of NeuroEngineering and Rehabilitation 18, no. 1 (June 29, 2021). http://dx.doi.org/10.1186/s12984-021-00888-2.
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AbstractTraumatic spinal cord injury (SCI) disrupts spinal and supraspinal pathways, and this process is reflected in changes in surface electromyography (sEMG). sEMG is an informative complement to current clinical testing and can capture the residual motor command in great detail—including in muscles below the level of injury with seemingly absent motor activities. In this comprehensive review, we sought to describe how the sEMG properties are changed after SCI. We conducted a systematic literature search followed by a narrative review focusing on sEMG analysis techniques and signal properties post-SCI. We found that early reports were mostly focused on the qualitative analysis of sEMG patterns and evolved to semi-quantitative scores and a more detailed amplitude-based quantification. Nonetheless, recent studies are still constrained to an amplitude-based analysis of the sEMG, and there are opportunities to more broadly characterize the time- and frequency-domain properties of the signal as well as to take fuller advantage of high-density EMG techniques. We recommend the incorporation of a broader range of signal properties into the neurophysiological assessment post-SCI and the development of a greater understanding of the relation between these sEMG properties and underlying physiology. Enhanced sEMG analysis could contribute to a more complete description of the effects of SCI on upper and lower motor neuron function and their interactions, and also assist in understanding the mechanisms of change following neuromodulation or exercise therapy.
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Liu, Yang, Yen-ting Chen, Chuan Zhang, Ping Zhou, Sheng Li, and Yingchun Zhang. "Motor unit distribution and recruitment in spastic and non-spastic bilateral biceps brachii muscles of chronic stroke survivors." Journal of Neural Engineering, August 4, 2022. http://dx.doi.org/10.1088/1741-2552/ac86f4.
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Abstract Objective- This study aims to characterize the motor units distribution and recruitment pattern in the spastic and non-spastic bilateral biceps brachii muscles (BBMs) of chronic stroke survivors. Approach- High-density surface electromyography (HD-sEMG) signals were collected from both spastic and non-spastic BBMs of fourteen chronic stroke subjects during isometric elbow flexion at 10%, 30%, 50% and 100% maximal voluntary contractions (MVCs). By combining HD-sEMG decomposition and bioelectrical source imaging, motor unit innervation zones (MUIZs) of the decomposed motor units were first localized in the 3D space of spastic and non-spastic BBMs. The motor unit depth defined as the distance between the localized MUIZ and its normal projection on the skin surface was then normalized to the arm radius of each subject and averaged at given contraction level. The averaged MU depth at different contraction levels on a specific arm side (intra-side) and the bilateral depths under a specific contraction level (inter-side) were compared. Main Results- The average depth of decomposed MUs increased with the contraction force and significant differences observed between 10% vs 50% (p < 0.0001), 10% vs 100% (p < 0.0001) and 30% vs 100% MVC (p = 0.0017) on the non-spastic side, indicating that larger MUs with higher recruitment threshold locate in deeper muscle regions. In contrast, no force-related difference in MU depth was observed on the spastic side, suggesting a disruption of orderly recruitment of MUs with increase of force level, or the MU denervation and the subsequent collateral reinnervation secondary to upper motor neuron lesions. Inter-side comparison demonstrated significant MU depth difference at 10% (p=0.0048) and 100% force effort (p=0.0026). Significance-This study represents the first effort to non-invasively characterize the MU distribution inside spastic and non-spastic bilateral BBM of chronic stroke patients by combining HD-sEMG recording, EMG signal decomposition and bioelectrical source imaging. The findings of this study advances our understanding regarding the neurophysiology of human muscles and the neuromuscular alterations following stroke. It may also offer important MU depth information for botulinum toxin (BoNT) injection in clinical post-stroke spasticity management.
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Cohen, Joshua W., Taian Vieira, Tanya D. Ivanova, and S. Jayne Garland. "Differential behaviour of distinct motoneuron pools that innervate the triceps surae." Journal of Neurophysiology, December 7, 2022. http://dx.doi.org/10.1152/jn.00336.2022.
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It has been shown that when humans lean in various directions, the central nervous system (CNS) recruits different motoneuron pools for task completion; common units that are active during different leaning directions, and unique units that are active in only one leaning direction. We used high-density surface electromyography (HD-sEMG) to examine if motor unit (MU) firing behaviour was dependent on leaning direction, muscle (medial and lateral gastrocnemius; soleus), limits of stability, or whether a MU is considered common or unique. Fourteen healthy participants stood on a force platform and maintained their center of pressure in five different leaning directions. HD-sEMG recordings were decomposed into MU action potentials and the average firing rate (AFR), coefficient of variation (CoVISI) and firing intermittency were calculated on the MU spike trains. During the leaning directions that demanded larger force production, both unique and common units had higher firing rates (F = 31.31, p < 0.0001). However, the unique units achieved higher firing rates compared to the common units (mean estimate difference = 3.48 Hz, p < 0.0001). The CoVISI increased across directions for the unique units but not for the common units (F = 23.65. p < 0.0001). Finally, intermittent activation of MUs was dependent on the leaning direction (F = 11.15, p < 0.0001), with less intermittent activity occurring during diagonal and forward-leaning directions. These results provide evidence that the CNS can preferentially control separate motoneuron pools within the ankle plantarflexors during voluntary leaning tasks for the maintenance of standing balance.
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Zhao, Nan, Bolun Zhao, Gencai Shen, Chunpeng Jiang, Zhuangzhuang Wang, Zude Lin, Lanshu Zhou, and Jingquan Liu. "A robust HD-sEMG sensor suitable for convenient acquisition of muscle activity in clinical post-stroke dysphagia." Journal of Neural Engineering, December 13, 2022. http://dx.doi.org/10.1088/1741-2552/acab2f.
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Abstract Objective. A flexible high-density surface electromyography (HD-sEMG) sensor combined with an adaptive algorithm was used to collect and analyze the swallowing activities of patients with post-stroke PSD. Approach. The electrode frame, modified electrode, and bonded substrate of the sensor were fabricated using a flexible printed circuit process, controlled drop coating, and molding, respectively. The adaptation algorithm was achieved by using Laplace and Teager-Kaiser energy operators to extract active segments, a cross-correlation coefficient matrix (CCCM) to evaluate synergy, and multi-frame real-time dynamic root mean square (RMS) to visualize spatiotemporal information to screen lesions. and level of dysphagia. Finally, support vector machines (SVM) were adopted to explore the classification accuracy of sex, age, and lesion location with small sample sizes. Main results. The sensor not only has a basic low contact impedance (0.262 kΩ) and high signal-to-noise ratio (37.284±1.088 dB) but also achieves other characteristics suitable for clinical applications, such as flexibility (747.67 kPa) and durability (1000 times) balance, simple operation (including initial, repeated, and replacement use), and low cost ($ 15.2). The three conclusions are as follows. CCCM can be used as a criterion for judging the unbalanced muscle region of the patient's neck and can accurately locate unbalanced muscles. The RMS cloud map provides the time consumption, swallowing times, and unbalanced areas. When the lesion location involves the left and right hemispheres simultaneously, it can be used as an evidence of relatively severely unbalanced areas. The classification accuracy of SVM in terms of sex, age, and lesion location was as high as 100%. Significance. The HD-sEMG sensor in this study and the adaptation algorithm will contribute to the establishment of a larger-scale database in the future to establish more detailed and accurate quantitative standards, which will be the basis for developing more optimized screening mechanisms and rehabilitation assessment methods.
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dos Anjos, Fabio Vieira, Gennaro Boccia, Paolo Riccardo Brustio, Alberto Rainoldi, and Marco Gazzoni. "Optimal bipolar system positioning to provide information about the trapezius activity associated with scapular retraction during shoulder exercises for resistance training." Physiological Measurement, September 30, 2022. http://dx.doi.org/10.1088/1361-6579/ac96cc.
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Abstract Objective: Of recent interest is the use of EMG biofeedback to make subjects aware of their stabilizers’ activation associated with scapular retraction during exercise, addressing challenges related to EMG detection. Whether there is an optimal bipolar positioning discriminating the stabilizers’ activation with retraction from neutral scapular position during resistance exercises is an open issue we addressed here by simultaneously mapping different positions using High-density surface electromyography (HD-sEMG). Approach: Sixteen resistance-trained males performed with and without scapular retraction five pulling exercises: barbell row, dumbbell row, pull-down at Lat machine, seated row, and TRX row. HD-sEMG was acquired in monopolar mode from medial and lower trapezius (8x4 electrodes and inter-electrode distance, ied: 10 mm) and different bipolar systems were simulated in terms of positioning, interelectrode distance and orientation with respect to the spine: longitudinal with three ieds (20 mm, 30 mm, and 40 mm), one transversal and two diagonals (ied: 20 mm), totalizing six EMGs’ sets. To identify the optimal electrode pair able to distinguish between the presence or absence of scapular retraction we computed: (i) the RMS map for each condition and the difference between them, obtaining a differential RMS map per subject; (ii) the intersection of cumulative maps, by summing the differential (binary) maps from all subjects. Main results: For the lower trapezius, results revealed that the diagonal direction (45 degrees; ied: 20 mm) obtained the greater occurrence of intersecting segments within and between exercises than the other electrode configurations, showing low variability for the optimal positioning across exercises. Electrode configuration varied within and between the pulling exercises for the medial trapezius. Significance: This study allowed to identify an optimal bipolar positioning (consistent across subjects and exercises) for lower trapezius activity assessment, representing a guideline for electrode positioning when EMG biofeedback for selective activation of the lower trapezius is adopted during pulling exercises.
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Lanza, Marcel B., Alice S. Ryan, Vicki Gray, William J. Perez, and Odessa Addison. "Intramuscular Fat Influences Neuromuscular Activation of the Gluteus Medius in Older Adults." Frontiers in Physiology 11 (December 10, 2020). http://dx.doi.org/10.3389/fphys.2020.614415.
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The amount of tissue between the muscle and surface electromyography (sEMG) electrode influences the sEMG signals. Increased intramuscular adipose tissue (IMAT) of the hip abductor muscles negatively impacts balance in older individuals, but it is unknown if this is related to the ability to activate the muscles. The aim of this preliminary study was to investigate the influence of gluteus medius (GM) IMAT on sEMG amplitude during maximal voluntary isometric contractions (MVIC) of the hip abductors in older adults. We recruited 12 healthy community-dwelling older adults that underwent a spiral computerized tomography scan. High density lean (HDL), IMAT, and subcutaneous adipose tissue (SUBFAT) cross-sectional area of the GM were assessed. sEMG signal from the GM was recorded while participants performed an MVIC of the hip abductors. There was a negative correlation between GM activation and IMAT (r = −0.58, P = 0.046), and also SUBFAT (r = −0.78, P = 0.002) and a positive correlation with HDL (r = 0.73, P = 0.006). When controlling for SUBFAT, the partial correlations demonstrated a consistent negative correlation between GM activation and IMAT (r = −0.60, P = 0.050) but no relationship with HDL. The current results are important for helping to interpret the results from sEMG by accounting for IMAT. In conclusion, the neuromuscular activation of the GM may be reduced by the quantity of IMAT.
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Zhang, Wenli, Tingsong Zhao, Jianyi Zhang, and Yufei Wang. "LST-EMG-Net: Long short-term transformer feature fusion network for sEMG gesture recognition." Frontiers in Neurorobotics 17 (February 28, 2023). http://dx.doi.org/10.3389/fnbot.2023.1127338.
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With the development of signal analysis technology and artificial intelligence, surface electromyography (sEMG) signal gesture recognition is widely used in rehabilitation therapy, human-computer interaction, and other fields. Deep learning has gradually become the mainstream technology for gesture recognition. It is necessary to consider the characteristics of the surface EMG signal when constructing the deep learning model. The surface electromyography signal is an information carrier that can reflect neuromuscular activity. Under the same circumstances, a longer signal segment contains more information about muscle activity, and a shorter segment contains less information about muscle activity. Thus, signals with longer segments are suitable for recognizing gestures that mobilize complex muscle activity, and signals with shorter segments are suitable for recognizing gestures that mobilize simple muscle activity. However, current deep learning models usually extract features from single-length signal segments. This can easily cause a mismatch between the amount of information in the features and the information needed to recognize gestures, which is not conducive to improving the accuracy and stability of recognition. Therefore, in this article, we develop a long short-term transformer feature fusion network (referred to as LST-EMG-Net) that considers the differences in the timing lengths of EMG segments required for the recognition of different gestures. LST-EMG-Net imports multichannel sEMG datasets into a long short-term encoder. The encoder extracts the sEMG signals’ long short-term features. Finally, we successfully fuse the features using a feature cross-attention module and output the gesture category. We evaluated LST-EMG-Net on multiple datasets based on sparse channels and high density. It reached 81.47, 88.24, and 98.95% accuracy on Ninapro DB2E2, DB5E3 partial gesture, and CapgMyo DB-c, respectively. Following the experiment, we demonstrated that LST-EMG-Net could increase the accuracy and stability of various gesture identification and recognition tasks better than existing networks.
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Kuruganti, Usha, Ashirbad Pradhan, and Jacqueline Toner. "High-Density Electromyography Provides Improved Understanding of Muscle Function for Those With Amputation." Frontiers in Medical Technology 3 (August 9, 2021). http://dx.doi.org/10.3389/fmedt.2021.690285.
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Transtibial amputation can significantly impact an individual's quality of life including the completion of activities of daily living. Those with lower limb amputations can harness the electrical activity from their amputated limb muscles for myoelectric control of a powered prosthesis. While these devices use residual muscles from transtibial-amputated limb as an input to the controller, there is little research characterizing the changes in surface electromyography (sEMG) signal generated by the upper leg muscles. Traditional surface EMG is limited in the number of electrode sites while high-density surface EMG (HDsEMG) uses multiple electrode sites to gather more information from the muscle. This technique is promising for not only the development of myoelectric-controlled prostheses but also advancing our knowledge of muscle behavior with clinical populations, including post-amputation. The HDsEMG signal can be used to develop spatial activation maps and features of these maps can be used to gain valuable insight into muscle behavior. Spatial features of HDsEMG can provide information regarding muscle activation, muscle fiber heterogeneity, and changes in muscle distribution and can be used to estimate properties of both the amputated limb and intact limb. While there are a few studies that have examined HDsEMG in amputated lower limbs they have been limited to movements such as gait. The purpose of this study was to examine the quadriceps muscle during a slow, moderate and fast isokinetic knee extensions from a control group as well as a clinical patient with a transtibial amputation. HDsEMG was collected from the quadriceps of the dominant leg of 14 young, healthy males (mean age = 25.5 ± 7 years old). Signals were collected from both the intact and amputated limb muscle of a 23 year old clinical participant to examine differences between the affected and unaffected leg. It was found that there were differences between the intact and amputated limb limb of the clinical participant with respect to muscle activation and muscle heterogeneity. While this study was limited to one clinical participant, it is important to note the differences in muscle behavior between the intact and amputated limb limb. Understanding these differences will help to improve training protocols for those with amputation.
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Kisiel-Sajewicz, Katarzyna, Jarosław Marusiak, Mónica Rojas-Martínez, Damian Janecki, Sławomir Chomiak, Łukasz Kamiński, Joanna Mencel, Miguel Ángel Mañanas, Artur Jaskólski, and Anna Jaskólska. "High-density surface electromyography maps after computer-aided training in individual with congenital transverse deficiency: a case study." BMC Musculoskeletal Disorders 21, no. 1 (October 15, 2020). http://dx.doi.org/10.1186/s12891-020-03694-4.
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Abstract Background The aim of this study was to determine whether computer-aided training (CAT) of motor tasks would increase muscle activity and change its spatial distribution in a patient with a bilateral upper-limb congenital transverse deficiency. We believe that our study makes a significant contribution to the literature because it demonstrates the usefulness of CAT in promoting the neuromuscular adaptation in people with congenital limb deficiencies and altered body image. Case presentation The patient with bilateral upper-limb congenital transverse deficiency and the healthy control subject performed 12 weeks of the CAT. The subject’s task was to imagine reaching and grasping a book with the hand. Subjects were provided a visual animation of that movement and sensory feedback to facilitate the mental engagement to accomplish the task. High-density electromyography (HD-EMG; 64-electrode) were collected from the trapezius muscle during a shrug isometric contraction before and after 4, 8, 12 weeks of the training. After training, we observed in our patient changes in the spatial distribution of the activation, and the increased average intensity of the EMG maps and maximal force. Conclusions These results, although from only one patient, suggest that mental training supported by computer-generated visual and sensory stimuli leads to beneficial changes in muscle strength and activity. The increased muscle activation and changed spatial distribution of the EMG activity after mental training may indicate the training-induced functional plasticity of the motor activation strategy within the trapezius muscle in individual with bilateral upper-limb congenital transverse deficiency. Marked changes in spatial distribution during the submaximal contraction in the patient after training could be associated with changes of the neural drive to the muscle, which corresponds with specific (unfamiliar for patient) motor task. These findings are relevant to neuromuscular functional rehabilitation in patients with a bilateral upper-limb congenital transverse deficiency especially before and after upper limb transplantation and to development of the EMG based prostheses.
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Tam, Simon, Mounir Boukadoum, Alexandre Campeau-Lecours, and Benoit Gosselin. "Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning." Scientific Reports 11, no. 1 (May 28, 2021). http://dx.doi.org/10.1038/s41598-021-90688-4.
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AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.
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Kim, Dongwon, Corine Nicoletti, Subaryani D. H. Soedirdjo, Raziyeh Baghi, Maria-Gabriela Garcia, Thomas Läubli, Pascal Wild, Alberto Botter, and Bernard J. Martin. "Effect of Periodic Voluntary Interventions on Trapezius Activation and Fatigue During Light Upper Limb Activity." Human Factors: The Journal of the Human Factors and Ergonomics Society, December 8, 2021, 001872082110507. http://dx.doi.org/10.1177/00187208211050723.
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Objective The effects of diverse periodic interventions on trapezius muscle fatigue and activity during a full day of computer work were investigated. Background Musculoskeletal disorders, including trapezius myalgia, may be associated with repeated exposure to prolonged low-level activity, even during light upper-extremity tasks including computer work. Methods Thirty healthy adults participated in a study that simulated two 6-hour workdays of computer work. One workday involved imposed periodic passive and active interventions aimed at disrupting trapezius contraction monotony (Intervention day), whereas the other workday did not (Control day). Trapezius muscle activity was quantified by the 3-dimensional acceleration of the jolt movement of the acromion produced by electrically induced muscle twitches. The spatio-temporal distribution of trapezius activity was measured through high-density surface electromyography (HD-EMG). Results The twitch acceleration magnitude in one direction was significantly different across measurement periods ( p = 0.0156) on Control day, whereas no significant differences in any direction were observed ( p > 0.05) on Intervention day. The HD-EMG from Intervention day showed that only significant voluntary muscle contractions (swing arms, Jacobson maneuver) induced a decrease in the muscle activation time and an increase in the spatial muscle activation areas ( p < 0.01). Conclusion Disruption of trapezius monotonous activity via brief voluntary contractions effectively modified the ensuing contraction pattern (twitch acceleration along one axis, active epochs reduction, and larger spatial distribution). The observed changes support an associated reduction of muscle fatigue. Application This study suggests that disruptive intervention activity is efficient in reducing the impact of trapezius muscle fatigue.
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Arvanitidis, Michail, David Jiménez-Grande, Nadège Haouidji-Javaux, Deborah Falla, and Eduardo Martinez-Valdes. "People with chronic low back pain display spatial alterations in high-density surface EMG-torque oscillations." Scientific Reports 12, no. 1 (September 7, 2022). http://dx.doi.org/10.1038/s41598-022-19516-7.
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AbstractWe quantified the relationship between spatial oscillations in surface electromyographic (sEMG) activity and trunk-extension torque in individuals with and without chronic low back pain (CLBP), during two submaximal isometric lumbar extension tasks at 20% and 50% of their maximal voluntary torque. High-density sEMG (HDsEMG) signals were recorded from the lumbar erector spinae (ES) with a 64-electrode grid, and torque signals were recorded with an isokinetic dynamometer. Coherence and cross-correlation analyses were applied between the filtered interference HDsEMG and torque signals for each submaximal contraction. Principal component analysis was used to reduce dimensionality of HDsEMG data and improve the HDsEMG-based torque estimation. sEMG-torque coherence was quantified in the δ(0–5 Hz) frequency bandwidth. Regional differences in sEMG-torque coherence were also evaluated by creating topographical coherence maps. sEMG-torque coherence in the δ band and sEMG-torque cross-correlation increased with the increase in torque in the controls but not in the CLBP group (p = 0.018, p = 0.030 respectively). As torque increased, the CLBP group increased sEMG-torque coherence in more cranial ES regions, while the opposite was observed for the controls (p = 0.043). Individuals with CLBP show reductions in sEMG-torque relationships possibly due to the use of compensatory strategies and regional adjustments of ES-sEMG oscillatory activity.
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Suhaimi, M. M., A. S. Ghazali, A. Jazlan, and S. N. Sidek. "Explication of Extrinsic Forearm Muscles On the Classification of Thumb Position Using High-Density Surface Electromyogram." International Journal of Integrated Engineering 15, no. 1 (April 5, 2023). http://dx.doi.org/10.30880/ijie.2023.15.01.002.
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Muscles for hand functions and movements play a major role in basic daily activities such aswriting and lifting objects. The main digit of the finger in differentiating the hand gesture is the thumb and its main muscles are intrinsic muscles. However, for transradial amputees, despite the loss of access to the intrinsic muscles, any information from the extrinsic muscles would be paramount and non-negotiablein creating a perfect hand prosthesis. As such, the research is dedicated to study the relationship between extrinsic muscles located athuman’sforearm to characterize the actual thumb attitudes.A 64-channel HD-sEMGrecording device together with a thumb force measuring platform wasutilizedtocollect the required signals from 17 participants at several thumb angle positions namely zero-degrees, thirty-degree, sixty-degrees, and ninety-degree. For each position, the participants were required to place their thumbs on top of a load cell at relax (no force at all) and contact (30% of their individual Maximum VoluntaryContraction or known as MVC) conditions repetitively by following a designated trajectory. Feature extraction was performed by calculating the Root Mean Square (RMS) values of the HD-sEMG data collected from each channel. Six different classifiers have been used to classify the relationship between the forearm HD-sEMG and the corresponding thumb positions.As a result, LazyIBK obtained the highest correctly classified instances with 81.05%. The finding is significant in developing a dedicated control framework for a prosthetic hand for tansradial amputees that can operate as closely as normal
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Wu, Le, Xun Chen, Xiang Chen, and Xu Zhang. "Rejecting Novel Motions in High-Density Myoelectric Pattern Recognition Using Hybrid Neural Networks." Frontiers in Neurorobotics 16 (March 28, 2022). http://dx.doi.org/10.3389/fnbot.2022.862193.
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The objective of this study is to develop a method for alleviating a novel pattern interference toward achieving a robust myoelectric pattern-recognition control system. To this end, a framework was presented for surface electromyogram (sEMG) pattern classification and novelty detection using hybrid neural networks, i.e., a convolutional neural network (CNN) and autoencoder networks. In the framework, the CNN was first used to extract spatio-temporal information conveyed in the sEMG data recorded via high-density (HD) 2-dimensional electrode arrays. Given the target motion patterns well-characterized by the CNN, autoencoder networks were applied to learn variable correlation in the spatio-temporal information, where samples from any novel pattern appeared to be significantly different from those from target patterns. Therefore, it was straightforward to discriminate and then reject the novel motion interferences identified as untargeted and unlearned patterns. The performance of the proposed method was evaluated with HD-sEMG data recorded by two 8 × 6 electrode arrays placed over the forearm extensors and flexors of 9 subjects performing seven target motion tasks and six novel motion tasks. The proposed method achieved high accuracies over 95% for identifying and rejecting novel motion tasks, and it outperformed conventional methods with statistical significance (p < 0.05). The proposed method is demonstrated to be a promising solution for rejecting novel motion interferences, which are ubiquitous in myoelectric control. This study will enhance the robustness of the myoelectric control system against novelty interference.
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Rojas-Martínez, Monica, Miguel A. Mañanas, and Joan F. Alonso. "High-density surface EMG maps from upper-arm and forearm muscles." Journal of NeuroEngineering and Rehabilitation 9, no. 1 (December 2012). http://dx.doi.org/10.1186/1743-0003-9-85.
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Abstract Background sEMG signal has been widely used in different applications in kinesiology and rehabilitation as well as in the control of human-machine interfaces. In general, the signals are recorded with bipolar electrodes located in different muscles. However, such configuration may disregard some aspects of the spatial distribution of the potentials like location of innervation zones and the manifestation of inhomogineties in the control of the muscular fibers. On the other hand, the spatial distribution of motor unit action potentials has recently been assessed with activation maps obtained from High Density EMG signals (HD-EMG), these lasts recorded with arrays of closely spaced electrodes. The main objective of this work is to analyze patterns in the activation maps, associating them with four movement directions at the elbow joint and with different strengths of those tasks. Although the activation pattern can be assessed with bipolar electrodes, HD-EMG maps could enable the extraction of features that depend on the spatial distribution of the potentials and on the load-sharing between muscles, in order to have a better differentiation between tasks and effort levels. Methods An experimental protocol consisting of isometric contractions at three levels of effort during flexion, extension, supination and pronation at the elbow joint was designed and HD-EMG signals were recorded with 2D electrode arrays on different upper-limb muscles. Techniques for the identification and interpolation of artifacts are explained, as well as a method for the segmentation of the activation areas. In addition, variables related to the intensity and spatial distribution of the maps were obtained, as well as variables associated to signal power of traditional single bipolar recordings. Finally, statistical tests were applied in order to assess differences between information extracted from single bipolar signals or from HD-EMG maps and to analyze differences due to type of task and effort level. Results Significant differences were observed between EMG signal power obtained from single bipolar configuration and HD-EMG and better results regarding the identification of tasks and effort levels were obtained with the latter. Additionally, average maps for a population of 12 subjects were obtained and differences in the co-activation pattern of muscles were found not only from variables related to the intensity of the maps but also to their spatial distribution. Conclusions Intensity and spatial distribution of HD-EMG maps could be useful in applications where the identification of movement intention and its strength is needed, for example in robotic-aided therapies or for devices like powered- prostheses or orthoses. Finally, additional data transformations or other features are necessary in order to improve the performance of tasks identification.
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Suhaimi, Muhammad Mukhlis, Aimi Shazwani Ghazali, Ahmad Jazlan, and Naim Sidek. "Analysis of High-Density Surface Electromyogram (HD-sEMG) signal for thumb posture classification from extrinsic forearm muscles." Cogent Engineering 9, no. 1 (April 12, 2022). http://dx.doi.org/10.1080/23311916.2022.2055445.
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