Littérature scientifique sur le sujet « Electromyographie de surface (sEMG) »
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Articles de revues sur le sujet "Electromyographie de surface (sEMG)"
Zieliński, Grzegorz, et Piotr Gawda. « Surface Electromyography in Dentistry—Past, Present and Future ». Journal of Clinical Medicine 13, no 5 (26 février 2024) : 1328. http://dx.doi.org/10.3390/jcm13051328.
Texte intégralFang, Yinfeng, Honghai Liu, Gongfa Li et Xiangyang Zhu. « A Multichannel Surface EMG System for Hand Motion Recognition ». International Journal of Humanoid Robotics 12, no 02 (27 mai 2015) : 1550011. http://dx.doi.org/10.1142/s0219843615500115.
Texte intégralBolek, Jeffrey E. « Uncommon Surface Electromyography ». Biofeedback 38, no 2 (1 juin 2010) : 52–55. http://dx.doi.org/10.5298/1081-5937-38.2.52.
Texte intégralHE, JINBAO, XINHUA YI et ZAIFEI LUO. « CHARACTERIZATION OF MOTOR UNIT AT DIFFERENT STRENGTHS WITH MULTI-CHANNEL SURFACE ELECTROMYOGRAPHY ». Journal of Mechanics in Medicine and Biology 17, no 01 (février 2017) : 1750024. http://dx.doi.org/10.1142/s0219519417500245.
Texte intégralArena, John G. « Future Directions in Surface Electromyography ». Biofeedback 38, no 2 (1 juin 2010) : 78–82. http://dx.doi.org/10.5298/1081-5937-38.2.78.
Texte intégralCoppeta, Luca, Sandro Gentili, Stefano Mugnaini, Ottavia Balbi, Stefano Massimiani, Gianluca Armieri, Antonio Pietroiusti et Andrea Magrini. « Neuromuscular Functional Assessment in Low Back Pain by Surface Electromyography (SEMG) ». Open Public Health Journal 12, no 1 (28 février 2019) : 61–67. http://dx.doi.org/10.2174/1874944501912010061.
Texte intégralDeprez, Kenneth, Eliah De Baecke, Mauranne Tijskens, Ruben Schoeters, Maarten Velghe et Arno Thielens. « A Circular, Wireless Surface-Electromyography Array ». Sensors 24, no 4 (8 février 2024) : 1119. http://dx.doi.org/10.3390/s24041119.
Texte intégralAnkrum, Dennis R. « Questions to ask When Interpreting Surface Electromyography (SEMG) Research ». Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no 30 (juillet 2000) : 5–530. http://dx.doi.org/10.1177/154193120004403036.
Texte intégralGomez-Correa, Manuela, et David Cruz-Ortiz. « Low-Cost Wearable Band Sensors of Surface Electromyography for Detecting Hand Movements ». Sensors 22, no 16 (9 août 2022) : 5931. http://dx.doi.org/10.3390/s22165931.
Texte intégralSella, Gabriel E. « Surface EMG (SEMG) : A Synopsis ». Biofeedback 47, no 2 (1 juin 2019) : 36–43. http://dx.doi.org/10.5298/1081-5937-47.1.05.
Texte intégralThèses sur le sujet "Electromyographie de surface (sEMG)"
Imrani, Sallak Loubna. « Evaluation of muscle aging using high density surface electromyography ». Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2647.
Texte intégralWith 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
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.
Texte intégralThe 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
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/.
Texte intégralAFSHARIPOUR, 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.
Texte intégralBEGNONI, 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.
Texte intégralZhao, Yuchen. « Human skill capturing and modelling using wearable devices ». Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/27613.
Texte intégralBerro, 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.
Texte intégralFast 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
Naik, Ganesh Ramachandra, et 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.
Texte intégralLienhard, 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.
Texte intégralThe 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
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.
Texte intégralLivres sur le sujet "Electromyographie de surface (sEMG)"
Merletti, Roberto, Catherine Disselhorst-Klug, William Zev Rymer et Isabella Campanini, dir. 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.
Texte intégralChapitres de livres sur le sujet "Electromyographie de surface (sEMG)"
Disselhorst-Klug, Catherine, Sybele Williams et Sylvie C. F. A. von Werder. « Surface Electromyography Meets Biomechanics or Bringing sEMG to Clinical Application ». Dans 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.
Texte intégralZeng, Cheng, Enhao Zheng, Qining Wang et Hong Qiao. « A Current-Based Surface Electromyography (sEMG) System for Human Motion Recognition : Preliminary Study ». Dans Intelligent Robotics and Applications, 737–47. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89095-7_70.
Texte intégralZhang, Bowen, Bingdie Huang, Qun Wu, Guowei Lu et Yao Wu. « Research on the Analysis of Muscle Fatigue Based on the Algorithm of Wavelet Packet Entropy in sEMG ». Dans Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220040.
Texte intégral« Recognition of sequential upper limb movements based on surface Electromyography (sEMG) signals ». Dans Bioinformatics and Biomedical Engineering : New Advances, 153–60. CRC Press, 2015. http://dx.doi.org/10.1201/b19238-27.
Texte intégralMarek, Martyna, et Magdalena Stania. « Analiza aktywności bioelektrycznej mięśni brzucha podczas ćwiczeń wg metody Pilates ». Dans 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.
Texte intégralRahim, Ku Nurhanim Ku Abdul, I. Elamvazuthi, P. Vasant et T. Ganesan. « Robotic Assistive System ». Dans 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.
Texte intégralRahim, Ku Nurhanim Ku Abdul, I. Elamvazuthi, P. Vasant et T. Ganesan. « Robotic Assistive System ». Dans Robotic Systems, 1688–720. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch081.
Texte intégralRodríguez Serrezuela, Ruthber, Enrique Marañón Reyes, Roberto Sagaró Zamora et Alexander Alexeis Suarez Leon. « Perspective Chapter : Classification of Grasping Gestures for Robotic Hand Prostheses Using Deep Neural Networks ». Dans Human-Robot Interaction - Perspectives and Applications [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.107344.
Texte intégralPrakash, Alok, et Shiru Sharma. « Development of an Affordable Myoelectric Hand for Transradial Amputees ». Dans 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.
Texte intégralMarcarian, David. « Protecting Bioelectric Signals from Electromagnetic Interference in a Wireless World ». Dans Biomedical Engineering. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105951.
Texte intégralActes de conférences sur le sujet "Electromyographie de surface (sEMG)"
Grammar, Alex W., et Robert L. Williams. « Surface Electromyographic Control of a Humanoid Robot ». Dans 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.
Texte intégralGuan, Wong Hooi, M. K. A. Ahamed Khan, Manickam Ramasamy, Chun Kit Ang, Lim Wei Hong, Kalaiselvi, C. Deisy, S. Sridevi et M. Suresh. « Surface Electromyography (SEMG) Based Robotic Assistive Device ». Dans 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation (ROMA). IEEE, 2022. http://dx.doi.org/10.1109/roma55875.2022.9915657.
Texte intégralDu, Yu, Yongkang Wong, Wenguang Jin, Wentao Wei, Yu Hu, Mohan Kankanhalli et Weidong Geng. « Semi-Supervised Learning for Surface EMG-based Gesture Recognition ». Dans 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.
Texte intégralElamvazuthi, I., G. A. Ling, K. A. R. Ku Nurhanim, P. Vasant et S. Parasuraman. « Surface electromyography (sEMG) feature extraction based on Daubechies wavelets ». Dans 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA 2013). IEEE, 2013. http://dx.doi.org/10.1109/iciea.2013.6566603.
Texte intégralOzturk, Ozberk, et Murat Kaya Yapici. « Muscular Activity Monitoring and Surface Electromyography (sEMG) with Graphene Textiles ». Dans 2019 IEEE SENSORS. IEEE, 2019. http://dx.doi.org/10.1109/sensors43011.2019.8956801.
Texte intégralPatil, Shailaja, et Shubhangi Patil. « Surface electromyography (sEMG) based pain intensity measurement using SVM algorithm ». Dans INTERNATIONAL CONFERENCE ON SMART MATERIALS AND STRUCTURES, ICSMS-2022. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0130353.
Texte intégralAhmed, Majeed Shihab, Asmiet Ramizy et Yousif Al Mashhadany. « An Analysis Review : Real Measurement for Surface Electromyography (sEMG) Signal ». Dans 2021 14th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 2021. http://dx.doi.org/10.1109/dese54285.2021.9719427.
Texte intégralImperatori, Giona, et Diego Barrettino. « A wireless surface electromyography (sEMG) probe with 4 high-speed channels ». Dans 2012 IEEE Sensors. IEEE, 2012. http://dx.doi.org/10.1109/icsens.2012.6411411.
Texte intégralSun, Qinglei, Zongtan Zhou, Jun Jiang et Dewen Hu. « Gait cadence detection based on surface electromyography (sEMG) of lower limb muscles ». Dans 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI). IEEE, 2014. http://dx.doi.org/10.1109/mfi.2014.6997665.
Texte intégralSri Sai Madhu Vinay Chowdary, Y., Jaswanth Reddy Tokala, Abhishek Sharma, Sanjeev Sharma et Vikas Sharma. « Artificial Intelligence-based Approach for Gait Pattern Identification Using Surface Electromyography (SEMG) ». Dans 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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