Academic literature on the topic 'Electromyographie de surface (sEMG)'
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Journal articles on the topic "Electromyographie de surface (sEMG)"
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Zieliński, Grzegorz, and Piotr Gawda. "Surface Electromyography in Dentistry—Past, Present and Future." Journal of Clinical Medicine 13, no. 5 (February 26, 2024): 1328. http://dx.doi.org/10.3390/jcm13051328.
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Surface electromyography (sEMG) is a technique for measuring and analyzing the electrical signals of muscle activity using electrodes placed on the skin’s surface. The aim of this paper was to outline the history of the development and use of surface electromyography in dentistry, to show where research and technical solutions relating to surface electromyography currently lie, and to make recommendations for further research. sEMG is a diagnostic technique that has found significant application in dentistry. The historical section discusses the evolution of sEMG methods and equipment, highlighting how technological advances have influenced the accuracy and applicability of this method in dentistry. The need for standardization of musculoskeletal testing methodology is highlighted and the needed increased technical capabilities of sEMG equipment and the ability to specify parameters (e.g., sampling rates, bandwidth). A higher sampling rate (the recommended may be 2000 Hz or higher in masticatory muscles) allows more accurate recording of changes in the signal, which is essential for accurate analysis of muscle function. Bandwidth is one of the key parameters in sEMG research. Bandwidth determines the range of frequencies effectively recorded by the sEMG system (the recommended frequency limits are usually between 20 Hz and 500 Hz in masticatory muscles). In addition, the increased technical capabilities of sEMG equipment and the ability to specify electromyographic parameters demonstrate the need for a detailed description of selected parameters in the methodological section. This is necessary to maintain the reproducibility of sEMG testing. More high-quality clinical trials are needed in the future.
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Fang, Yinfeng, Honghai Liu, Gongfa Li, and Xiangyang Zhu. "A Multichannel Surface EMG System for Hand Motion Recognition." International Journal of Humanoid Robotics 12, no. 02 (May 27, 2015): 1550011. http://dx.doi.org/10.1142/s0219843615500115.
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Surface electromyography (sEMG)-based hand motion recognition has a variety of promising applications. While a person performs different hand motions, commands can be extracted to control external devices, such as prosthetic hands, tablets and so forth. The acquisition of discriminative sEMG signals determines the accuracy of intended control commands extraction. This paper develops an 16-channel sEMG signal acquisition system with a novel electrode configuration that is specially designed to collect sEMG on the forearm. Besides, to establish the relationship between multichannel sEMG signals and hand motions, a 2D EMG map is designed. Inspired from the electromyographic (EMG) map, this paper proposes an EMG feature named differential root mean square (DRMS) that somewhat takes the relationship between neighboring EMG channels into account. In the task of four hand motion discrimination by K-means and fuzzy C-means, DRMS outperforms traditional root mean square (RMS) by 29.0% and 36.8%, respectively. The findings of this paper support and guide the use of sEMG techniques to investigate sEMG-based hand motion recognition.
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Bolek, Jeffrey E. "Uncommon Surface Electromyography." Biofeedback 38, no. 2 (June 1, 2010): 52–55. http://dx.doi.org/10.5298/1081-5937-38.2.52.
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Abstract Surface electromyography (SEMG) can be used as a tool to help gain the return/discovery of motor function in those with disabilities. This article presents the case of “Joey,” an 18-month-old toddler. An already challenging case due to age is made even more difficult considering his genetically based multiple impairments. SEMG provided a window of opportunity, previously unavailable, to allow Joey to demonstrate the new motor skills that he was capable of learning.
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HE, JINBAO, XINHUA YI, and ZAIFEI LUO. "CHARACTERIZATION OF MOTOR UNIT AT DIFFERENT STRENGTHS WITH MULTI-CHANNEL SURFACE ELECTROMYOGRAPHY." Journal of Mechanics in Medicine and Biology 17, no. 01 (February 2017): 1750024. http://dx.doi.org/10.1142/s0219519417500245.
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In this study, specific changes in electromyographic characteristics of individual motor units (MUs) associated with different muscle contraction forces are investigated using multi-channel surface electromyography (SEMG). The gradient convolution kernel compensation (GCKC) algorithm is employed to separate individual MUs from their surface interferential electromyography (EMG) signals and provide the discharge instants, which is later used in the spike-triggered averaging (STA) techniques to obtain the complete waveform. The method was tested on experimental SEMG signals acquired during constant force contractions of biceps brachii muscles in five subjects. Electromyographic characteristics including the recruitment number, waveform amplitude, discharge pattern and innervation zone (IZ) are studied. Results show that changes in the action potential of single MU with different contraction force levels are consistent with those for all MUs, and that the amplitude of MU action potentials (MUAPs) provides a useful estimate of the muscle contraction forces.
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Arena, John G. "Future Directions in Surface Electromyography." Biofeedback 38, no. 2 (June 1, 2010): 78–82. http://dx.doi.org/10.5298/1081-5937-38.2.78.
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Abstract The use of surface electromyography (SEMG) has increased exponentially in the past four decades. SEMG is one of the most widespread measures employed today in psychophysiological assessment and one of three primary biofeedback modalities. This article briefly outlines three areas that the author believes are important for SEMG to address if it is to continue to flourish in the future: applications in telehealth, the use of telemetry and ambulatory monitoring, and studies on the stability or reliability of surface electromyography.
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Coppeta, Luca, Sandro Gentili, Stefano Mugnaini, Ottavia Balbi, Stefano Massimiani, Gianluca Armieri, Antonio Pietroiusti, and Andrea Magrini. "Neuromuscular Functional Assessment in Low Back Pain by Surface Electromyography (SEMG)." Open Public Health Journal 12, no. 1 (February 28, 2019): 61–67. http://dx.doi.org/10.2174/1874944501912010061.
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Background: Low back pain is a major occupational health issue and a leading cause of disability globally. Significant differences in Surface Electromyography (SEMG) have been reported between persons with Low Back Pain (LBP) and normal, healthy controls. Many studies reveal that when the trunk is in full flexion there is an electrical silence in back muscles referred to as “flexion-relaxation phenomenon.” It is often absent in individuals reporting LBP and particularly chronic LBP. There are several SEMG measures that describe this phenomenon. Objective: To evaluate muscle activity in acute and chronic LBP and the usefulness of quick and reliable procedures to demonstrate abnormal electromyographic activity of the spine erector muscles. Methods: We evaluated 40 subjects aged 25-65 years. For each participant, a clinical history regarding the presence of chronic or acute LBP was collected. Each subject was evaluated with SEMG measures of spine erector muscles during standing and prone position (for acute LBP), and flex-extension movement (for chronic LBP subjects). Superficial potential was recorded and compared between groups. Results: In all three procedures, differences were identified in the surface electromyographic activity between the healthy controls and the one affected by LBP. Conclusion: The study of normal and pathologic electromyographic patterns could be a valid means to support in an objective way the presence/absence of acute and chronic LBP.
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Deprez, Kenneth, Eliah De Baecke, Mauranne Tijskens, Ruben Schoeters, Maarten Velghe, and Arno Thielens. "A Circular, Wireless Surface-Electromyography Array." Sensors 24, no. 4 (February 8, 2024): 1119. http://dx.doi.org/10.3390/s24041119.
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Commercial, high-tech upper limb prostheses offer a lot of functionality and are equipped with high-grade control mechanisms. However, they are relatively expensive and are not accessible to the majority of amputees. Therefore, more affordable, accessible, open-source, and 3D-printable alternatives are being developed. A commonly proposed approach to control these prostheses is to use bio-potentials generated by skeletal muscles, which can be measured using surface electromyography (sEMG). However, this control mechanism either lacks accuracy when a single sEMG sensor is used or involves the use of wires to connect to an array of multiple nodes, which hinders patients’ movements. In order to mitigate these issues, we have developed a circular, wireless s-EMG array that is able to collect sEMG potentials on an array of electrodes that can be spread (not) uniformly around the circumference of a patient’s arm. The modular sEMG system is combined with a Bluetooth Low Energy System on Chip, motion sensors, and a battery. We have benchmarked this system with a commercial, wired, state-of-the-art alternative and found an r = 0.98 (p < 0.01) Spearman correlation between the root-mean-squared (RMS) amplitude of sEMG measurements measured by both devices for the same set of 20 reference gestures, demonstrating that the system is accurate in measuring sEMG. Additionally, we have demonstrated that the RMS amplitudes of sEMG measurements between the different nodes within the array are uncorrelated, indicating that they contain independent information that can be used for higher accuracy in gesture recognition. We show this by training a random forest classifier that can distinguish between 6 gestures with an accuracy of 97%. This work is important for a large and growing group of amputees whose quality of life could be improved using this technology.
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Ankrum, Dennis R. "Questions to ask When Interpreting Surface Electromyography (SEMG) Research." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 30 (July 2000): 5–530. http://dx.doi.org/10.1177/154193120004403036.
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Surface electromyography (SEMG) is widely used to evaluate muscle activity. In SEMG, researchers attach electrodes to the surface of the skin overlying a muscle and measure the amount of electricity it produces as muscle fibers contract. SEMG can determine which muscles are active, their degree of activity, and how active the muscle is compared to the subject's capacity. It can also be used to estimate muscle force. Properly employed, SEMG assists in evaluating the relative risk of a work task. As articles reporting SEMG results are often used by ergonomics practitioners as guidance in job design, the ability to interpret SEMG research is critical. Problems occur when researchers assume their readers have a greater familiarity with SEMG than actually exists, or when they make any of a number of SEMG-related research or interpretation errors. This paper suggests some questions that should be asked when evaluating a study that reports SEMG data.
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Gomez-Correa, Manuela, and David Cruz-Ortiz. "Low-Cost Wearable Band Sensors of Surface Electromyography for Detecting Hand Movements." Sensors 22, no. 16 (August 9, 2022): 5931. http://dx.doi.org/10.3390/s22165931.
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Surface electromyography (sEMG) is a non-invasive measure of electrical activity generated due to muscle contraction. In recent years, sEMG signals have been increasingly used in diverse applications such as rehabilitation, pattern recognition, and control of orthotic and prosthetic systems. This study presents the development of a versatile multi-channel sEMG low-cost wearable band system to acquire 4 signals. In this case, the signals acquired with the proposed device have been used to detect hand movements. However, the WyoFlex band could be used in some sections of the arm or the leg if the section’s diameter matches the diameter of the WyoFlex band. The designed WyoFlex band was fabricated using three-dimensional (3D) printing techniques employing thermoplastic polyurethane and polylactic acid as manufacturing materials. Then, the proposed wearable electromyographic system (WES) consists of 2 WyoFlex bands, which simultaneously allow the wireless acquisition of 4 sEMG channels of each forearm. The collected sEMG can be visualized and stored for future post-processing stages using a graphical user interface designed in Node-RED. Several experimental tests were conducted to verify the performance of the WES. A dataset with sEMG collected from 15 healthy humans has been obtained as part of the presented results. In addition, a classification algorithm based on artificial neural networks has been implemented to validate the usability of the collected sEMG signals.
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Sella, Gabriel E. "Surface EMG (SEMG): A Synopsis." Biofeedback 47, no. 2 (June 1, 2019): 36–43. http://dx.doi.org/10.5298/1081-5937-47.1.05.
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Surface electromyography is an electrophysiological modality assessing the electrical activity of skeletal musculature. The Sella protocol is a structured assessment protocol, including static muscle assessment and dynamic muscle assessment, utilizing standardized electrode placements, conditions, and movements during assessment. This protocol can serve as a basis for designing biofeedback-assisted rehabilitation of patients with chronic pain and other musculoskeletal problems. The protocol can also be applied in forensic evaluations and in optimal performance settings.
Dissertations / Theses on the topic "Electromyographie de surface (sEMG)"
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Imrani, Sallak Loubna. "Evaluation of muscle aging using high density surface electromyography." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2647.
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Avec le vieillissement de la population, préserver la fonction musculaire est important pour éviter la perte de mobilité et d'autonomie. De nos jours, la prévention de la maladie musculaire, la sarcopénie, est une préoccupation majeure et des facteurs de risque importants tels que l'âge avancé ainsi que des facteurs modifiables, notamment une faible activité physique et une alimentation déséquilibrée ont été identifiés. Compte tenu de la croissance des populations plus âgées et de la diminution de l'activité physique, qui touche également les jeunes citoyens, la sensibilisation à la qualité musculaire peut être cruciale pour promouvoir un vieillissement en bonne santé dans nos sociétés. Les besoins en évaluations fonctionnelles musculaires ont été exprimés par les chercheurs et les cliniciens. Le groupe de travail européen sur la sarcopénie chez les personnes âgées (EWGSOP) recommande de définir la sarcopénie comme la présence à la fois d'une faible masse musculaire et d'une faible fonction musculaire (force et performance physique). Pour cela, nous avons développé une méthode d’évaluation du vieillissement musculaire, en utilisant une technologie ambulatoire et non invasive, appelée technologie d'électromyographie de surface haute densité (HD-sEMG), à travers un projet de recherche clinique sur cinq catégories d'âge (25 à 74 ans), actifs et sédentaires. Nous avons réalisé une étude comparative avec une analyse complète et multimodale du rectus femoris (RF), muscle impliqué dans les mouvements de la vie quotidienne, pour dévoiler le potentiel prometteur de la technique HD sEMG, par rapport aux techniques cliniques classiques, l’objectif étant de détecter les changements précoces de la qualité de la fonction musculaire impactée par le vieillissement et le niveau d'activité physique. La partie clinique de ce projet de thèse a été financée par une subvention européenne, EIT Health. En analysant principalement la dynamique de contraction musculaire et l'intensité du rectus femoris, nos résultats ont montré que la technique HD-sEMG, était capable de discriminer entre les cinq catégories d'âge de sujets sains physiquement actifs. Plus intéressant encore, les scores HD-sEMG proposés discriminaient entre les participants actifs et sédentaires, de la même catégorie d'âge (45-54 ans), contrairement aux paramètres cliniques et aux autres techniques couramment utilisées (absortiométrie biphotonique par rayons X, DXA et échographie). De plus, ces scores pour les participants sédentaires de cette catégorie d'âge étaient significativement plus proches de ceux des participants actifs des catégories d'âge supérieures (55-64 ans et 65-74 ans). Cela suggère fortement qu'un mode de vie sédentaire semble accélérer le processus de vieillissement musculaire au niveau anatomique et fonctionnel, et ce processus accéléré subtil peut être détecté par la technique HD-sEMG. Ces résultats préliminaires prometteurs pourraient contribuer au développement d’un outil intéressant aux cliniciens pour améliorer à la fois la précision et la sensibilité de l'évaluation musculaire utile pour les programmes de prévention et de réadaptation afin d'éviter ou de retarder la sarcopénie, problème de santé publique actuel alerté par l'Organisation Mondiale de la Santé (OMS) et promouvoir un vieillissement en bonne santéWith the aging of the population, preserving muscle function is important to prevent loss of mobility and autonomy. Nowadays, the prevention of the muscle disease, sarcopenia, is a major concern and important risk factors such as older age as well as modifiable factors including low physical activity and unhealthy diet have been identified. Considering the growth of older populations and the decreased physical activity, which also includes young citizens, muscle quality awareness can be crucial in promoting a healthy aging process in our societies. Muscle functional assessments needs were expressed by researchers and clinicians, The European Working Group on Sarcopenia in Older People (EWGSOP) recommends defining sarcopenia as the presence of both low muscle mass and low muscle function (strength, and physical performance). For this purpose, we have developed a method for muscle aging evaluation, using an ambulatory and non-invasive technology, called high-density surface electromyography (HDsEMG), through a clinical research project on five age categories (25 to 74 yrs.). We performed a comparative study with a complete and multimodal analysis of the rectus femoris, muscle involved in daily life motions, in order to reveal the promising potential of the HD-sEMG technique, compared to conventional clinical techniques, to detect early changes in the quality of muscle function impacted by aging and physical activity level. The clinical part of this thesis project was funded by a European grant, EITH Health. By analyzing both muscle contraction dynamics and intensity of the rectus femoris, our results showed that the HD-sEMG technique, was able to discriminate between the five age categories of healthy physically active subjects. More interestingly, the proposed HD-sEMG scores discriminated between active and sedentary participants, from the same age category(45-54 yrs.), in contrary to clinical parameters and others usual techniques (dual-energy x-ray absorptiometry, DXA and ultrasonography). In addition, these scores for sedentary participants from this age category were significantly closer to those of active participants from higher age categories (55-64 yrs. and 65-74 yrs.). This strongly suggests that sedentary lifestyle seems to accelerate the muscle aging process at both anatomical and functional level, and this subtle accelerated process can be detected by the HD-sEMG technique. These promising preliminary results can contribute to the development of an interesting tool for clinicians to improve both accuracy and sensitivity of functional muscle evaluation useful for prevention and rehabilitation to avoid the effects of unhealthy lifestyle that can potentially lead to sarcopenia. This can support also the actual public health concern alerted by Word Health Organization (WHO) regarding aging and sarcopenia, to promote healthy aging
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Douania, Inès. "Multi-scales, multi-physics personalized HD-sEMG model for the evaluation of skeletal muscle aging." Electronic Thesis or Diss., Compiègne, 2022. http://www.theses.fr/2022COMP2679.
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Le vieillissement musculaire, en tant qu'entité pathologique, est connu sous le nom de sarcopénie. Il est défini comme une réduction de la force musculaire accompagnée d'une perte de masse musculaire et d'un déclin des fonctions physiques. Les méthodologies actuellement utilisées en pratique clinique pour évaluer cette maladie liée au vieillissement sont plutôt limitées pour saisir les caractéristiques de ce déclin à l'échelle macroscopique: mesure de la force et de la masse musculaire. Cependant, diagnostiquer la sarcopénie en mesurant uniquement ces deux paramètres n'est pas assez précis et ne permet pas de détecter une perte précoce de la fonction musculaire. Il est plus fiable d'exploiter des changements musculaires à l’échelle microscopique: tels que la perte des unités motrices (l'unité motrice (UM) est constituée d'un motoneurone et de toutes les fibres musculaires squelettiques innervées par ses terminaisons axonales), l'atrophie des fibres, le désordre de la commande neuronale, et l’infiltration intramusculaire des cellules adipeuses. Ainsi, des études récentes, basées sur la technique d'électromyographie de surface (sEMG), ont démontré le grand potentiel de cette technique en tant que biomarqueur pour détecter les premiers signes de muscles sarcopéniques. En effet, le signal sEMG est la réponse électrique de l'activation musculaire gérée par le système nerveux central (SNC). Il est mesuré de manière non invasive à la surface de la peau à l'aide d'électrodes de surface et peut être efficacement corrélé à la réponse mécanique de l'activation musculaire. De plus, les modèles mathématiques du signal sEMG peuvent former une alliance utile avec les mesures expérimentales et le traitement du sEMG pour identifier et/ou quantifier les bio-indicateurs (c'est-à-dire les paramètres anatomiques et neuronaux des muscles) d'un vieillissement musculaire sain, précoce, accéléré ou sarcopénique. Dans cette thèse, nous avons utilisé un modèle électrique optimisé décrivant l'activité électrique du muscle à la surface de la peau à l'aide de la technique d'électromyographie à haute densité (HD-sEMG). Ce modèle est développé précédemment dans notre laboratoire de recherches. Le temps de calcul réduit de ce modèle est l'élément clé majeur pour effectuer l'identification des indicateurs de vieillissement à l'aide de méthodes inverses et de la technique HD-sEMG. Cependant, cette identification nécessite des méthodes préalables telles que l'analyse de sensibilité et d'identifiabilité. De plus, lors de l'utilisation de ce modèle, nous avons observé d'importantes limitations telles qu'un manque de réalisme physiologique (par exemple, les territoires des UM et le nombre de fibres par muscle), de personnalisation (par exemple, le même schéma de recrutement neuronal pour les sujets jeunes et âgés) et de simplicité (par exemple, l'ajustement de 50 paramètres de modèle en fonction de l'âge et du sexe). Ces limitations restreignent l'utilisation de ce modèle dans le diagnostic du vieillissement musculaire. Par conséquent, l'objectif de cette thèse est de remédier à ces limitations et de fournir un modèle plus réaliste et simple à utiliser pour évaluer le vieillissement musculaire. Dans ce travail, nous proposons d'abord une méthode d’analyse de sensibilité de Morris améliorée (IMSA) appliquée au modèle développé. Cette analyse a été réalisée sur des sujets simulés jeunes et âgés (à faible et à fort niveau de contractions). Grâce à cette IMSA, nous avons réussi à mettre en évidence et avec précision les paramètres/facteurs neuromusculaires influents pour chaque catégorie d'âge, à chaque niveau de force et pour chaque descripteur statistique calculée à partir des signaux HD-sEMG. De plus, grâce à l'IMSA, nous avons mis en évidence les limitations du modèle mentionnées précédemment. Pour remédier à ces limitations, nous avons modifié le schéma du modèle pour le rendre plus facile à manipuler, avec moins de risques d'erreurs et d'incohérencesThe muscle aging, as a disease entity, is known as Sarcopenia. It is defined as a reduction of muscle strength/force accompanied by a loss of muscle mass and a decline in physical functions. The current methodologies used in clinical practice to assess this aging disease, are rather limited to capture the features of this decline at the macroscopic scale. Factors such as the loss of Motor Units (motor unit (MU) is made up of a motoneuron and all the skeletal muscle fibers innervated by the neuron's axon terminals), the atrophy of fibers and the disorder of the neural recruitment pattern are shown to have a clear influence on muscular function. However, diagnosing sarcopenia by only measuring the muscle strength and/or muscle mass is not enough accurate and cannot alert an early loss of muscular function. The inner scales (MU and fiber scale age-related changes) reflecting that loss of muscle mass and strength during aging are more interesting to exploit. Thus, recent studies, based on the surface electromyography (sEMG) technique, have demonstrated the great potential of this technique to be used as a biomarker to detect early signs of sarcopenic muscles. In fact, the sEMG signal is the electrical response of the muscle activation managed by the Central Nervous System (CNS). It is measured with a noninvasive manner at the skin surface using surface electrodes and can be correlated efficiently to the mechanical response of muscle activation. Moreover, mathematical models of sEMG signal can form a useful alliance with sEMG experimental measures and processing to identify and/or quantify bio-indicators (i.e., anatomical, and neural muscle parameters) of a healthy, early, accelerated or sarcopenic muscle aging. In this thesis work, we have used a fast and optimized electrical model describing the electrical activity of the muscle at the skin surface using High Density sEMG technique (HD-sEMG), developed in our laboratory team. The reduced computational time of this model is the major key feature to perform the identification of aging indicators using inverse methods and HD-sEMG technique. However, this identification needs pre-aided-methods such as the sensitivity and the identifiability analysis. Moreover, when dealing with this model, we have observed important limitations such as lack of physiological realism (e.g., MUS territories and the number of fibers per muscle), personalization (e.g., same recruitment pattern for young and elder subject), and simplicity (e.g., adjustment of 50 model parameters according to age and gender). These limitations restrain the use of this model in muscle aging diagnosis. Therefore, we aimed in this thesis to address the limitations of this model and deliver more realistic and user-friendly model to evaluate muscle aging. Therefore, in this work, we first propose an Improved Morris Sensitivity Analysis (IMSA) applied on the developed model. This analysis was performed on young and elder simulated subjects (at low and high force level). Using this IMSA, we success to spotlight with accuracy the influential neuromuscular parameters/factors for each age category, at each force level, and for each statistic feature computed over the HD-sEMG signals. Furthermore, using IMSA, we have outlined the model inaccuracies and limitations mentioned above. To address these limitations, we have modified the model schema implementation to be easier to manipulate (user-friendly model), with less error and inconsistency risks. Only the age and the gender of subject became needed as model entries to initiate a simulation of HD-sEMG signals. All other parameters necessary in simulations are then estimated through "statistical" models. The statistical models employ regression analysis to estimate the relation Parameter versus Age. A bibliographic research reporting these morphological and structural changes according to age, gender, and Biceps Brachii muscle was done
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Zanghieri, Marcello. "sEMG-based hand gesture recognition with deep learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18112/.
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Hand gesture recognition based on surface electromyographic (sEMG) signals is a promising approach for the development of Human-Machine Interfaces (HMIs) with a natural control, such as intuitive robot interfaces or poly-articulated prostheses. However, real-world applications are limited by reliability problems due to motion artifacts, postural and temporal variability, and sensor re-positioning.
This master thesis is the first application of deep learning on the Unibo-INAIL dataset, the first public sEMG dataset exploring the variability between subjects, sessions and arm postures, by collecting data over 8 sessions of each of 7 able-bodied subjects executing 6 hand gestures in 4 arm postures. In the most recent studies, the variability is addressed with training strategies based on training set composition, which improve inter-posture and inter-day generalization of classical (i.e. non-deep) machine learning classifiers, among which the RBF-kernel SVM yields the highest accuracy.
The deep architecture realized in this work is a 1d-CNN implemented in Pytorch, inspired by a 2d-CNN reported to perform well on other public benchmark databases. On this 1d-CNN, various training strategies based on training set composition were implemented and tested.
Multi-session training proves to yield higher inter-session validation accuracies than single-session training. Two-posture training proves to be the best postural training (proving the benefit of training on more than one posture), and yields 81.2% inter-posture test accuracy. Five-day training proves to be the best multi-day training, and yields 75.9% inter-day test accuracy. All results are close to the baseline. Moreover, the results of multi-day trainings highlight the phenomenon of user adaptation, indicating that training should also prioritize recent data.
Though not better than the baseline, the achieved classification accuracies rightfully place the 1d-CNN among the candidates for further research.
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AFSHARIPOUR, BABAK. "Estimation of load sharing among muscles acting on the same joint and Applications of surface electromyography." Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2535698.
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The force produced by a specific muscle cannot be measured and what is measured is the total force provided by all the active muscles acting on a joint, including agonists and antagonists. The first part of this work (chapter 3) addresses the issue of load sharing by proposing two possible approaches and testing them. The second part (chapter 4 and 5) addresses two applications of surface EMG focusing on the study of a) muscle relaxation associated to Yoga sessions and b) the activation of muscle of the back and shoulder of musicians playing string instruments (violin, viola and cello).
In both parts the element of innovation is the use of two dimensional electrode arrays and of techniques based on EMG Imaging. The objectives of this work are presented and explained in chapter 1 while the basic concepts of surface EMG are summarized in chapter 2. Different EMG-based muscle force models found in the literature are explained and discussed.
Two renowned amplitude indicators in surface EMG (sEMG) studies are the average rectified value (ARV) and the root mean square (RMS). These two amplitude indicators are computed over a defined time window of the recorded signals to represent the muscle activity. The advantages and disadvantages of RMS and ARV are compared and discussed for a simple sinusoid as well as for more complex signals (simulated motor unit action potential detected by high density electrode grid). The results show that RMS is more robust to the sampling frequency than ARV.
In this thesis, starting from the simulation of a single fiber and of a group of fibers (motor unit), it is shown that inter electrode distance (IED) greater than10 mm causes aliasing. Aliasing is a source of error in sEMG map interpretation or decisions that are made by automatic algorithms such as those providing image segmentation for the identifications of regions of interest.
Chapter 2 discusses three segmentation algorithms (K-means, h-dome, watershed) and compares them in order to find the most suitable method. Results reveal that among the three mentioned algorithms, watershed provides most accurate segmentation for the simulated ARV maps.
Chapter 3 presents a mathematical model that is associated to the monotonic Force-EMG relation. A possible non-linear relationship between the EMG and force or torque is presented.
A system of "M" equations is obtained by performing "M" measurements at "M" different force levels in isometric conditions. The solutions of such system of equations are the values for each muscles. Two different approaches were investigated for finding the solutions of the system, which are: a) Analytical-Graphical Approach (AGA) and b) Numerical Approach (NA) consisting of error minimization (between the total estimated and measured force) applying optimization algorithms.
The AGA was used to find the model parameters of each muscle contributing to the force production on a joint by finding the intersection of those surfaces that can be obtained from sequential substitutions of the model parameters in the equations corresponding to each contraction level. In simulation studies, the AGA graphically shows that there is more than one solution to the load sharing problem even for the simplest theoretical case (i.e. a joint spanned by only two muscles). The second approach, based on minimization of the mean square error between the measured and the total estimated force or torque (with "N" muscles involved) provides an estimate of the model parameters that in turn provides the force contributions of the individual muscles. The optimization algorithms can find the solutions of our system made of non-linear equations (see chapter 3). Starting from different point (initial conditions), different solutions can be found, as predicted by the AGA approach for the two-muscle case. The main conclusion of this study is that the load sharing strategy is not unique.
Chapter 4 discusses the application of surface electromyography to a single case study of Yoga relaxation to show the feasibility of measurements. The effect of yoga relaxation on muscle activity (sEMG amplitude), as well as on mean and median frequencies and muscle fiber's conduction Velocity, is discussed in this chapter. No changes in the sEMG activity pattern distribution were found for the same task performed before and after applying yoga relaxation technique. However, myoelectric manifestations of fatigue were smaller after relaxation and returned to the normal pattern after the recovery phase from relaxation. Further studies are justified.
Chapter 5 describes results and discusses the spatial distribution of muscle activity over the Trapezius and Erector Spinae muscles of musicians playing string instruments. In chapter 5, the effect of backrest support in sitting position during playing cello, viola, and violin on the muscle activity index of upper and lower Trapezius muscle of the bowing arm, upper Trapezius muscle of non-bowing arm, left and right Erector Spinae muscles is investigated. Two professional players (one cello and one viola) and five student players (one cello, three violin and one viola) participated in this study. The muscle activity index (MAI) was defined as the spatial average of RMS values of the muscle active region detected by watershed segmentation for Trapezius muscles (left and right), and thresholding technique (70% of the maximum value) for left and right Erector Spinae muscles. It was found that the MAI is string (note) dependent. Statistical difference (p < 0:05) between the MAIs of left Erector Spinae muscle during playing with and without backrest support was observed in four (out of five) student players. No statistical differences were observed on the muscle activity of Trapezius (bowing and no-bowing arms) during playing with and without backrest support in different types of bowing for all musicians.
In conclusion, this work addresses a) the issue of spatial sampling and segmentation of sEMG using 2D electrode arrays, b) two possible approaches to the load-sharing issue, c) a single-case study of Yoga relaxation and d) the distribution of muscle activity above the Trapezius and Erector Spinae muscles of musicians playing string instruments. Previously unavailable knowledge has been achieved in all these four studies.
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BEGNONI, GIACOMO. "ELECTROMYOGRAPHIC EVALUATION OF THE EFFICACY OF MYOFUNCTIONAL THERAPY IN PATIENTS WITH ATYPICAL SWALLOWING." Doctoral thesis, Università degli Studi di Milano, 2018. http://hdl.handle.net/2434/618978.
Full textAbstract:
ABSTRACT
Objectives:
Swallowing is a complex physiologic function developing mostly in the first years of life. After 6 years old, if a complete maturation is not achieved, swallowing persists as “atypical swallowing” (AS). The therapy provided to re-educate this dysfunction is based on the myofunctional treatment (MFT). The aim of this study was to detect functional (electromyographical) and clinical (orofacial muscular evaluation with score (OMEs) protocol) effects of MFT in a group of patients with AS so to highlight any differences in the muscular activation pattern and muscular orofacial behavior.
Materials and Methods:
20 adolescents and young adults (4 males and 16 females, mean age 17.85 years, SD 4.80) with AS were selected for this study. Standardized surface electromyographic (ssEMG) analysis was performed by the same operator to detect the activity of masseter (MM), temporalis (TA) and submental (SM) muscles before (T1) and after (T2) the logopedic treatment. The MFT was performed by the same speech therapist according to the Garliner method for a period of 10 weeks. The speech therapist completed the OMEs protocol at T1 and T2. A Student-t test for paired data was carried out to detect differences between T1 and T2 for both ssEMG and OMEs data. Then, a 1-way ANOVA variance test was performed to detect any differences between the different couples of muscles at T1 and T2. In addition, ssEMG data at T1 and T2 were compared with ssEMG obtained in a control (C) group of 18 adolescents and young adult patients (8 males and 10 females, mean age 17.28 years, SD 2.56) with bimolar class 1 and without AS.
Results:
From the starting group of 20 patients, 15 patients completed the MFT (4 males and 11 females, mean age 17.72 years, SD 5.21). At T2, AS patients showed a significantly shorter duration of activation for each couple of muscles and for the whole duration act of swallowing (p<.0001) as well as higher intensity of the SM activity (p<.01) than at T1. Within the AS group, at T1 the masticatory muscles (MM and TA) showed lower duration of activation (p<.05) and lower intensity of the spike (p<.0001) than SM. At T2, masticatory muscles also showed lower values for the activation index (IMPACT) (p<.0001) and for the spike position (p<.01) than SM. At T2. The OMEs protocol showed a significant increase for the total evaluation (p<.01) and specifically for appearance and posture (p<.01) and functions (p<.0001). If compared to C group, the AS group at T1 showed significantly longer duration of activation for each couple of muscles and for the whole duration act of swallowing (p<.0001) as well as lower intensity of the SM activity (p<.05) than controls. At T2 all the ssEMG data detected in AS patients showed a general improvement and moved toward the control values. The differences between AS and C groups about the duration of activation of each couple of muscles and the whole duration act of swallowing were lower at T2 than at T1 even if still significantly different from C ones (p<.0001).
Conclusion:
MFT confirms itself as an effective method in the treatment of AS dysfunction permitting a shortening of the muscular activation pattern, an increase in SM activity and a general improvement in the orofacial muscular behavior making them closer to the data obtained in controls. ssEMG and OMEs protocol represent valid and useful methods in the analysis of the swallowing function and in establishing the effects of the MFT.
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Zhao, Yuchen. "Human skill capturing and modelling using wearable devices." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/27613.
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Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to fulfil all the requirements because even a relatively simple task such as a peg-in-hole insertion contains many uncertainties, e.g. clearance, initial grasping position and insertion path. Humans, on the other hand, can deal with these variations using their vision and haptic feedback. Although humans can adapt to uncertainties easily, most of the time, the skilled based performances that relate to their tacit knowledge cannot be easily articulated. Even though the automation solution may not fully imitate human motion since some of them are not necessary, it would be useful if the skill based performance from a human could be firstly interpreted and modelled, which will then allow it to be transferred to the robot. This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Recently, Learning from Demonstration (LfD) is gaining interest as a framework to transfer skills from human teacher to robot using probability encoding approaches to model observations and state transition uncertainties. In close or actual contact manipulation tasks, it is difficult to reliabley record the state-action examples without interfering with the human senses and activities. Therefore, wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. Firstly to track human motions accurately and reliably in a defined 3-dimensional workspace, a hybrid system of Vicon and IMUs is proposed to compensate for the known limitations of the individual system. The data fusion method was able to overcome occlusion and frame flipping problems in the two camera Vicon setup and the drifting problem associated with the IMUs. The results indicated that occlusion and frame flipping problems associated with Vicon can be mitigated by using the IMU measurements. Furthermore, the proposed method improves the Mean Square Error (MSE) tracking accuracy range from 0.8˚ to 6.4˚ compared with the IMU only method. Secondly, to record haptic feedback from a teacher without physically obstructing their interactions with the workpiece, wearable surface electromyography (sEMG) armbands were used as an indirect method to indicate contact feedback during manual manipulations. A muscle-force model using a Time Delayed Neural Network (TDNN) was built to map the sEMG signals to the known contact force. The results indicated that the model was capable of estimating the force from the sEMG armbands in the applications of interest, namely in peg-in-hole and beater winding tasks, with MSE of 2.75N and 0.18N respectively. Finally, given the force estimation and the motion trajectories, a Hidden Markov Model (HMM) based approach was utilised as a state recognition method to encode and generalise the spatial and temporal information of the skilled executions. This method would allow a more representative control policy to be derived. A modified Gaussian Mixture Regression (GMR) method was then applied to enable motions reproduction by using the learned state-action policy. To simplify the validation procedure, instead of using the robot, additional demonstrations from the teacher were used to verify the reproduction performance of the policy, by assuming human teacher and robot learner are physical identical systems. The results confirmed the generalisation capability of the HMM model across a number of demonstrations from different subjects; and the reproduced motions from GMR were acceptable in these additional tests. The proposed methodology provides a framework for producing a state-action model from skilled demonstrations that can be translated into robot kinematics and joint states for the robot to execute. The implication to industry is reduced efforts and time in programming the robots for applications where human skilled performances are required to cope robustly with various uncertainties during tasks execution.
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Berro, Soumaya. "Identification of muscle activation schemes by inverse methods applied on HD-sEMG signals." Electronic Thesis or Diss., Compiègne, 2022. http://www.theses.fr/2022COMP2708.
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L'identification rapide ou en temps réel de l'activation spatio-temporelle des unités motrices (UM) qui représentent les unités fonctionnelles du système neuromusculaire est fondamentale dans les applications de contrôle des prothèses et en réhabilitation fonctionnelle. Cependant, cette procédure demande un temps de calcul énorme. Par conséquent, le travail de cette thèse a été consacré à fournir un algorithme permettant l'identification en temps réel des stratégies d'activation spatiale et temporelle des UMs en appliquant des méthodes inverses sur les signaux HD-sEMG (électromyogramme de surface à haute densité) à partir d'une grille placée sur le Biceps Brachial (BB). À cette fin, nous proposons une approche innovante, qui implique l'utilisation de la méthode inverse classique de minimisation de norme et une interpolation de courbe en 3D, à savoir l'approche est nommée CFB-MNE. Cette méthode, fondée sur l'identification inverse (estimation de la norme minimale) couplée à un dictionnaire des potentiels d'action des unités motrices simulées (MUAP) d'un modèle récent et testée sur des simulations, a permis la localisation en temps réel des unités motrices individuelles simulées. Une analyse de robustesse (modifications anatomiques, physiologiques et instrumentales) a ensuite été effectuée pour vérifier l'efficacité de l'algorithme proposé. Enfin, l'algorithme proposé a été testé sur des UMs avec des schémas de recrutement réalistes donnant des résultats prometteurs et encourageants en identification spatiale et temporelle sur trois scenarios. Pour conclure, en perspectives, les résultats prometteurs obtenus suggèrent l'utilisation de l'apprentissage automatique et de l'intelligence artificielle (IA) pour améliorer encore les performances de l'algorithme proposéFast or real-time identification of the spatiotemporal activation of Motor Units (MUs), functional units of the neuromuscular system, is fundamental in applications as prosthetic control and rehabilitation guidance but often dictates expensive computational times. Therefore, the thesis work was devoted to providing an algorithm that enables the real-time identification of MU spatial and temporal activation strategies by applying inverse methods on HD-sEMG (high-density surface electromyogram) signals from a grid placed over the Biceps Brachii (BB). For this purpose, we propose an innovative approach, that involves the use of the classical minimum norm inverse method and a 3D fitting curve interpolation, namely CFB-MNE approach. This method, based on inverse identification (minimum norm estimation) coupled to simulated motor unit action potential (MUAP) dictionary from a recent model and tested on simulations, allowed the real time localization of simulated individual motor units. A robustness analysis (anatomical, physiological, and instrumental modifications) was then performed to verify the efficiency of the proposed algorithm. Finally, the proposed algorithm was tested on MUs with realistic recruitment patterns giving promising results in both spatial and temporal identification. To conclude, a door to future perspectives was opened, according to the obtained promising results, suggesting the use of machine learning and artificial intelligence (AI) to further boost the performance of the proposed algorithm
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Naik, Ganesh Ramachandra, and ganesh naik@rmit edu au. "Iterative issues of ICA, quality of separation and number of sources: a study for biosignal applications." RMIT University. Electrical and Computer Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090320.115103.
Full textAbstract:
This thesis has evaluated the use of Independent Component Analysis (ICA) on Surface Electromyography (sEMG), focusing on the biosignal applications. This research has identified and addressed the following four issues related to the use of ICA for biosignals: The iterative nature of ICA The order and magnitude ambiguity problems of ICA Estimation of number of sources based on dependency and independency nature of the signals Source separation for non-quadratic ICA (undercomplete and overcomplete) This research first establishes the applicability of ICA for sEMG and also identifies the shortcomings related to order and magnitude ambiguity. It has then developed, a mitigation strategy for these issues by using a single unmixing matrix and neural network weight matrix corresponding to the specific user. The research reports experimental verification of the technique and also the investigation of the impact of inter-subject and inter-experimental variations. The results demonstrate that while using sEMG without separation gives only 60% accuracy, and sEMG separated using traditional ICA gives an accuracy of 65%, this approach gives an accuracy of 99% for the same experimental data. Besides the marked improvement in accuracy, the other advantages of such a system are that it is suitable for real time operations and is easy to train by a lay user. The second part of this thesis reports research conducted to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The work proposes the use of value of the determinant of the Global matrix generated using sparse sub band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures. The results support the applications such as human computer interface. This thesis has also developed a method of determining the number of independent sources in a given mixture and has also demonstrated that using this information, it is possible to separate the signals in an undercomplete situation and reduce the redundancy in the data using standard ICA methods. The experimental verification has demonstrated that the quality of separation using this method is better than other techniques such as Principal Component Analysis (PCA) and selective PCA. This has number of applications such as audio separation and sensor networks.
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Lienhard, Karin. "Effet de l'exercice physique par vibration du corps entier sur l'activité musculaire des membres inférieurs : approche méthodologique et applications pratiques." Thesis, Nice, 2014. http://www.theses.fr/2014NICE4080/document.
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L’objectif de cette thèse a été d’analyser l’effet de l’exercice physique réalisé sur plateforme vibrante (whole-body vibration, WBV) sur l’activité musculaire des membres inférieurs, de développer des outils d’analyse méthodologiques et de proposer des recommandations pratiques d’utilisation. Deux études méthodologiques ont été menées pour identifier la méthode optimale permettant de traiter les signaux d'électromyographie de surface (sEMG) recueillis pendant la vibration et d'analyser l'influence de la méthode de normalisation de l'activité sEMG. Une troisième étude visait à mieux comprendre si les pics sEMG observés dans le spectre de puissance du signal contiennent des artéfacts de mouvement et/ou de l'activité musculaire réflexe. Les trois études suivantes avaient pour but de quantifier l’effet de la WBV sur l’activité musculaire en fonction de différents paramètres tels que, la fréquence de vibration, l'amplitude de la plateforme, une charge supplémentaire, le type de plateforme, l'angle articulaire du genou, et la condition physique du sujet. En outre, l'objectif a été de déterminer l'accélération verticale minimale permettant de stimuler au mieux l'activité musculaire des membres inférieurs. En résumé, les recherches menées au cours de cette thèse fournissent des solutions pour de futures études sur : i) comment supprimer les pics dans le spectre du signal sEMG et, ii) comment normaliser l'activité musculaire pendant un exercice WBV. Enfin, les résultats de cette thèse apportent à la littérature scientifique de nouvelles recommandations pratiques liées à l’utilisation des plateformes vibrantes à des fins d’exercice physiqueThe aim of this thesis was to analyze the effect of whole-body vibration (WBV) exercise on lower limb muscle activity and to give methodological implications and practical applications. Two methodological studies were conducted that served to evaluate the optimal method to process the surface electromyography (sEMG) signals during WBV exercise and to analyze the influence of the normalization method on the sEMG activity. A third study aimed to gain insight whether the isolated spikes in the sEMG spectrum contain motion artifacts and/or reflex activity. The subsequent three investigations aimed to explore how the muscle activity is affected by WBV exercise, with a particular focus on the vibration frequency, platform amplitude, additional loading, platform type, knee flexion angle, and the fitness status of the WBV user. The final goal was to evaluate the minimal required vertical acceleration to stimulate the muscle activity of the lower limbs. In summary, the research conducted for this thesis provides implication for future investigations on how to delete the excessive spikes in the sEMG spectrum and how to normalize the sEMG during WBV. The outcomes of this thesis add to the current literature in providing practical applications for exercising on a WBV platform
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LION, BREUIL VALERIE. "Apport de l'etude de l'activite electrique musculaire de surface a l'aide d'un materiel portable au cours de tests d'effort chez le sportif." Amiens, 1991. http://www.theses.fr/1991AMIEM116.
Full textBooks on the topic "Electromyographie de surface (sEMG)"
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Merletti, Roberto, Catherine Disselhorst-Klug, William Zev Rymer, and Isabella Campanini, eds. Surface Electromyography: Barriers Limiting Widespread use of sEMG in Clinical Assessment and Neurorehabilitation. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88966-616-4.
Full textBook chapters on the topic "Electromyographie de surface (sEMG)"
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Disselhorst-Klug, Catherine, Sybele Williams, and Sylvie C. F. A. von Werder. "Surface Electromyography Meets Biomechanics or Bringing sEMG to Clinical Application." In Converging Clinical and Engineering Research on Neurorehabilitation III, 1013–16. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01845-0_203.
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Zeng, Cheng, Enhao Zheng, Qining Wang, and Hong Qiao. "A Current-Based Surface Electromyography (sEMG) System for Human Motion Recognition: Preliminary Study." In Intelligent Robotics and Applications, 737–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89095-7_70.
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Zhang, Bowen, Bingdie Huang, Qun Wu, Guowei Lu, and Yao Wu. "Research on the Analysis of Muscle Fatigue Based on the Algorithm of Wavelet Packet Entropy in sEMG." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220040.
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Surface Electromyography (sEMG) has been widely applied in different fields, such as human-computer interaction and medical rehabilitation. This paper deeply studies the theoretical research of muscle fatigue analysis and scientific experiments of sEMG, especially related to muscle fatigue. It provides a theoretical basis for not only building an assessment method of muscle fatigue but also testing the relationship between the value of wavelet packet entropy and the complexity of signal frequency. Additionally, the field value of muscle fatigue has been uncovered. In this research, the experiment tests the energy variation of wavelet packet entropy when testing the muscle contraction by the sEMG signals of brachioradialis. It means that the experiment decomposes frequency band, and then sEMG is analyzed by the entropy and distribution of energy. The result of experiment indicated that the index of wavelet packet entropy has an efficient and quick performance on analyzing complexity of signal system. In the experiment of muscle fatigue, wavelet packet entropy can present high accuracy, instant reaction, stronger consistency and reliability, which is significant for the achievement of the real-time monitoring and clinical research of bioelectrical signals.
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"Recognition of sequential upper limb movements based on surface Electromyography (sEMG) signals." In Bioinformatics and Biomedical Engineering: New Advances, 153–60. CRC Press, 2015. http://dx.doi.org/10.1201/b19238-27.
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Marek, Martyna, and Magdalena Stania. "Analiza aktywności bioelektrycznej mięśni brzucha podczas ćwiczeń wg metody Pilates." In Wybrane badania naukowe w kulturze fizycznej. Tom 1, 35–45. Wydawnictwo Uniwersytetu Rzeszowskiego, 2023. http://dx.doi.org/10.15584/978-83-8277-057-5.3.
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The Pilates exercises might be used as a complementary therapy in physiotherapy practice for various dysfunctions. The aim of this study was to evaluate bioelectrical activity of the rectus abdominis (RA) and external oblique (EO) muscles with surface electromyography (sEMG) during Pilates exercises and their modifications. 13 students at the age range of 20-24 years participated in the experiment. The bioelectrical activity of RA and EO muscles was recorded during three exercises according to Pilates method (‘hundred’, ‘bridging’ and ‘standing balance’). Each exercise was performed in several experimental conditions: with no additional equipment, with a small ball and Pilates ring, on a stable and unstable surface. The average value of sEMG amplitude was normalized to the maximum voluntary contraction (%MVC).The average amplitude (%MVC) of EO and RA muscles was significantly the highest during ‘hundred’ exercise, both for stable and unstable condition, regardless the aspect of the use of additional equipment (p<0.001). U-Mann Whitney’s test revealed no significant differences in bioelectrical activity of both muscles during Pilates exercises for different experimental conditions. The use of additional equipment for Pilates exercises does not significantly affect the bioelectrical activity of the RA and EO muscles.
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Rahim, Ku Nurhanim Ku Abdul, I. Elamvazuthi, P. Vasant, and T. Ganesan. "Robotic Assistive System." In Handbook of Research on Human-Computer Interfaces, Developments, and Applications, 444–77. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0435-1.ch018.
Full textAbstract:
Stroke is the leading cause of disability that influences the quality of people's daily life. As such, an effective method is required for post-stroke rehabilitation. Research has shown that a robot is a good rehabilitation alternative where conventional robotic assistive system is encoded program by the robot expertise. The major drawback of this approach is that the lack of voluntary movement of the patient may affect the proficiency of the recovery process. Ideally, the robotic assistive system should recognize the intended movement and assist the patient to perform and make the training exercises more effective for recovery process. The electromyography based robotics assistive technology would enable the stroke patients to control the robot movement, according to the user's own strength of natural movement. This chapter briefly discusses the establishment of mathematical models based on artificial intelligent techniques that maps the surface electromyography (sEMG) signals to estimated joint torque of elbow for robotic assistive system.
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Rahim, Ku Nurhanim Ku Abdul, I. Elamvazuthi, P. Vasant, and T. Ganesan. "Robotic Assistive System." In Robotic Systems, 1688–720. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch081.
Full textAbstract:
Stroke is the leading cause of disability that influences the quality of people's daily life. As such, an effective method is required for post-stroke rehabilitation. Research has shown that a robot is a good rehabilitation alternative where conventional robotic assistive system is encoded program by the robot expertise. The major drawback of this approach is that the lack of voluntary movement of the patient may affect the proficiency of the recovery process. Ideally, the robotic assistive system should recognize the intended movement and assist the patient to perform and make the training exercises more effective for recovery process. The electromyography based robotics assistive technology would enable the stroke patients to control the robot movement, according to the user's own strength of natural movement. This chapter briefly discusses the establishment of mathematical models based on artificial intelligent techniques that maps the surface electromyography (sEMG) signals to estimated joint torque of elbow for robotic assistive system.
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Rodríguez Serrezuela, Ruthber, Enrique Marañón Reyes, Roberto Sagaró Zamora, and Alexander Alexeis Suarez Leon. "Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses Using Deep Neural Networks." In Human-Robot Interaction - Perspectives and Applications [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.107344.
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This research compares classification accuracy obtained with the classical classification techniques and the presented convolutional neural network for the recognition of hand gestures used in robotic prostheses for transradial amputees using surface electromyography (sEMG) signals. The first two classifiers are the most used in the literature: support vector machines (SVM) and artificial neural networks (ANN). A new convolutional neural network (CNN) architecture based on the AtzoriNet network is proposed to assess performance according to amputation-related variables. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods and The performance it is compared with other CNN proposed by other authors. The performance of the CNN is evaluated with different metrics, providing good results compared to those proposed by other authors in the literature.
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Prakash, Alok, and Shiru Sharma. "Development of an Affordable Myoelectric Hand for Transradial Amputees." In Research Anthology on Emerging Technologies and Ethical Implications in Human Enhancement, 352–64. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8050-9.ch017.
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Upper limb amputations seriously affect a patient's life by restricting their ability in performing various tasks. Prosthetic hands are considered the primary method to reinstate the lost capabilities of such amputees. However, the presently available prosthetic devices are unable to fulfill the requirements of users due to their excessively high cost, limited functionality, heavy weight, unnatural operation, and complexity. This article presents an affordable and simple control-based myoelectric hand for transradial amputees. The hand setup mainly consists of a self-designed surface electromyography (sEMG) sensor, a microcontroller unit and a five-fingered, intrinsically actuated 3D printed hand for dexterous operations. The developed hand was implemented with proportional control scheme and was successfully tested on five amputees (with missing lower forearms) for performing grasping activities of different objects. Further, the closing time and grip force at the fingertips were also determined for the hand to compare its performance with the commercially available hands.
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Marcarian, David. "Protecting Bioelectric Signals from Electromagnetic Interference in a Wireless World." In Biomedical Engineering. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105951.
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The exponential growth of wired and wireless technologies which generate Electromagnetic Interference (EMI) has made obtaining microvolt-level bioelectric signals challenging. While digital filtering algorithms provide a wealth of information and allow Artificial Intelligence (AI) to interpret the data, the process may denigrate the integrity of the original signal. Busy clinicians and researchers have relied on computer-analyzed ECG, losing their ability to discriminate between data of high quality and data contaminated with EMI (noise). Resolving an EMI issue with a microphone is one way to learn the methodology. A step-by-step process of troubleshooting EMI in an audio application provides a framework for understanding the fundamental variables that generate EMI and a better understanding of analog electronics. The troubleshooting methodology applies to resolving EMI issues with all biologic signals including Surface Electromyography (SEMG), EEG, ECG, and Needle EMG. As we enter the age of extended range WIFI and cellular technologies, understanding analog electronics is crucial in ensuring we obtain clean data for more clinically meaningful results.
Conference papers on the topic "Electromyographie de surface (sEMG)"
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Grammar, Alex W., and Robert L. Williams. "Surface Electromyographic Control of a Humanoid Robot." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13345.
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This paper details the development of an open-source surface electromyographic interface for controlling 1-DOF for the DARwIn-OP humanoid robot. This work also details the analysis of the relationship between surface electromyographic activity of the Biceps Brachii muscle and the angle of the elbow joint for the pseudo-static unloaded arm case. The human arm was mechanically modeled for a two link system actuated by a single muscle. The SEMG activity was found to be directly proportional to joint angle using a combination of custom joint angle measuring hardware and a surface electromyographic measuring circuit. This relationship allowed for straightforward control of the robot elbow joint directly. The interface was designed around the Arduino Microcontroller; another open-source platform. Software for the Arduino and DARwIn-OP were drawn from open source resources, allowing the entire system to be comprised of open-source components. A final surface electromyographic measuring and signal conditioning circuit was constructed. Data recording and processing software was also coded for the Arduino, thus achieving control of the robotic platform via surface electromyography.
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Guan, Wong Hooi, M. K. A. Ahamed Khan, Manickam Ramasamy, Chun Kit Ang, Lim Wei Hong, Kalaiselvi, C. Deisy, S. Sridevi, and M. Suresh. "Surface Electromyography (SEMG) Based Robotic Assistive Device." In 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation (ROMA). IEEE, 2022. http://dx.doi.org/10.1109/roma55875.2022.9915657.
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Du, Yu, Yongkang Wong, Wenguang Jin, Wentao Wei, Yu Hu, Mohan Kankanhalli, and Weidong Geng. "Semi-Supervised Learning for Surface EMG-based Gesture Recognition." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/225.
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Conventionally, gesture recognition based on non-intrusive muscle-computer interfaces required a strongly-supervised learning algorithm and a large amount of labeled training signals of surface electromyography (sEMG). In this work, we show that temporal relationship of sEMG signals and data glove provides implicit supervisory signal for learning the gesture recognition model. To demonstrate this, we present a semi-supervised learning framework with a novel Siamese architecture for sEMG-based gesture recognition. Specifically, we employ auxiliary tasks to learn visual representation; predicting the temporal order of two consecutive sEMG frames; and, optionally, predicting the statistics of 3D hand pose with a sEMG frame. Experiments on the NinaPro, CapgMyo and csl-hdemg datasets validate the efficacy of our proposed approach, especially when the labeled samples are very scarce.
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Elamvazuthi, I., G. A. Ling, K. A. R. Ku Nurhanim, P. Vasant, and S. Parasuraman. "Surface electromyography (sEMG) feature extraction based on Daubechies wavelets." In 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA 2013). IEEE, 2013. http://dx.doi.org/10.1109/iciea.2013.6566603.
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Ozturk, Ozberk, and Murat Kaya Yapici. "Muscular Activity Monitoring and Surface Electromyography (sEMG) with Graphene Textiles." In 2019 IEEE SENSORS. IEEE, 2019. http://dx.doi.org/10.1109/sensors43011.2019.8956801.
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Patil, Shailaja, and Shubhangi Patil. "Surface electromyography (sEMG) based pain intensity measurement using SVM algorithm." In INTERNATIONAL CONFERENCE ON SMART MATERIALS AND STRUCTURES, ICSMS-2022. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0130353.
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Ahmed, Majeed Shihab, Asmiet Ramizy, and Yousif Al Mashhadany. "An Analysis Review : Real Measurement for Surface Electromyography (sEMG) Signal." In 2021 14th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 2021. http://dx.doi.org/10.1109/dese54285.2021.9719427.
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Imperatori, Giona, and Diego Barrettino. "A wireless surface electromyography (sEMG) probe with 4 high-speed channels." In 2012 IEEE Sensors. IEEE, 2012. http://dx.doi.org/10.1109/icsens.2012.6411411.
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Sun, Qinglei, Zongtan Zhou, Jun Jiang, and Dewen Hu. "Gait cadence detection based on surface electromyography (sEMG) of lower limb muscles." In 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI). IEEE, 2014. http://dx.doi.org/10.1109/mfi.2014.6997665.
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Sri Sai Madhu Vinay Chowdary, Y., Jaswanth Reddy Tokala, Abhishek Sharma, Sanjeev Sharma, and Vikas Sharma. "Artificial Intelligence-based Approach for Gait Pattern Identification Using Surface Electromyography (SEMG)." In 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). IEEE, 2020. http://dx.doi.org/10.1109/ants50601.2020.9342795.
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