Academic literature on the topic 'Surface Electromyography (sEMG)'
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Journal articles on the topic "Surface Electromyography (sEMG)"
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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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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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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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Nacpil, Edric John Cruz, Rencheng Zheng, Tsutomu Kaizuka, and Kimihiko Nakano. "A surface electromyography controlled steering assistance interface." Journal of Intelligent and Connected Vehicles 2, no. 1 (August 29, 2019): 1–13. http://dx.doi.org/10.1108/jicv-11-2018-0011.
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Purpose Two-handed automobile steering at low vehicle speeds may lead to reduced steering ability at large steering wheel angles and shoulder injury at high steering wheel rates (SWRs). As a first step toward solving these problems, this study aims, firstly, to design a surface electromyography (sEMG) controlled steering assistance interface that enables hands-free steering wheel rotation and, secondly, to validate the effect of this rotation on path-following accuracy. Design/methodology/approach A total of 24 drivers used biceps brachii sEMG signals to control the steering assistance interface at a maximized SWR in three driving simulator scenarios: U-turn, 90º turn and 45º turn. For comparison, the scenarios were repeated with a slower SWR and a game steering wheel in place of the steering assistance interface. The path-following accuracy of the steering assistance interface would be validated if it was at least comparable to that of the game steering wheel. Findings Overall, the steering assistance interface with a maximized SWR was comparable to a game steering wheel. For the U-turn, 90º turn and 45º turn, the sEMG-based human–machine interface (HMI) had median lateral errors of 0.55, 0.3 and 0.2 m, respectively, whereas the game steering wheel, respectively, had median lateral errors of 0.7, 0.4 and 0.3 m. The higher accuracy of the sEMG-based HMI was statistically significant in the case of the U-turn. Originality/value Although production automobiles do not use sEMG-based HMIs, and few studies have proposed sEMG controlled steering, the results of the current study warrant further development of a sEMG-based HMI for an actual automobile.
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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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Nacpil, Edric John Cruz, and Kimihiko Nakano. "Surface Electromyography-Controlled Automobile Steering Assistance." Sensors 20, no. 3 (February 2, 2020): 809. http://dx.doi.org/10.3390/s20030809.
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Disabilities of the upper limb, such as hemiplegia or upper limb amputation, can limit automobile drivers to steering with one healthy arm. For the benefit of these drivers, recent studies have developed prototype interfaces that realized surface electromyography (sEMG)-controlled steering assistance with path-following accuracy that has been validated with driving simulations. In contrast, the current study expands the application of sEMG-controlled steering assistance by validating the Myo armband, a mass-produced sEMG-based interface, with respect to the path-following accuracy of a commercially available automobile. It was hypothesized that one-handed remote steering with the Myo armband would be comparable or superior to the conventional operation of the automobile steering wheel. Although results of low-speed field testing indicate that the Myo armband had lower path-following accuracy than the steering wheel during a 90° turn and wide U-turn at twice the minimum turning radius, the Myo armband had superior path-following accuracy for a narrow U-turn at the minimum turning radius and a 45° turn. Given its overall comparability to the steering wheel, the Myo armband could be feasibly applied in future automobile studies.
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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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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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Sella, Gabriel E. "Surface EMG (SEMG): A Synopsis." Biofeedback 47, no. 2 (June 1, 2019): 36–43. http://dx.doi.org/10.5298/1081-5937-47.1.05.
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Surface electromyography is an electrophysiological modality assessing the electrical activity of skeletal musculature. The Sella protocol is a structured assessment protocol, including static muscle assessment and dynamic muscle assessment, utilizing standardized electrode placements, conditions, and movements during assessment. This protocol can serve as a basis for designing biofeedback-assisted rehabilitation of patients with chronic pain and other musculoskeletal problems. The protocol can also be applied in forensic evaluations and in optimal performance settings.
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Zeng, Xiong, Ying Dong, and Xiaohao Wang. "Flexible Electrode by Hydrographic Printing for Surface Electromyography Monitoring." Materials 13, no. 10 (May 19, 2020): 2339. http://dx.doi.org/10.3390/ma13102339.
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Surface electromyography (sEMG) monitoring has recently inspired new applications in the field of patient diagnose, rehabilitation therapy, man–machine–interface and prosthesis control. However, conventional wet electrodes for sEMG recording cannot fully satisfy the requirements of these applications because they are based on rigid metals and conductive gels that cause signal quality attenuation, motion artifact and skin allergy. In this study, a novel flexible dry electrode is presented for sEMG monitoring. The electrode is fabricated by screen-printing a silver–eutectic gallium–indium system over a transfer tattoo paper, which is then hydrographically printed on 3D surface or human skin. Peano curve in open-network pattern is adopted to enhance the mechanics of the electrode. Hydrographic printing enables the electrode to attach to skin intimately and conformably, meanwhile assures better mechanical and electrical properties and therefore improves the signal quality and long-term wearability of the electrode. By recording sEMG signal of biceps under three kinds of movement with comparison to conventional wet electrode, the feasibility of the presented flexible dry electrode for sEMG monitoring was proved.
Dissertations / Theses on the topic "Surface Electromyography (sEMG)"
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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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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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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.
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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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Souza, Gustavo Souto de Sá e. "Arranjo linear de dez eletrodos ativos sem fio para eletromiografia de superfície." Universidade Federal de Goiás, 2013. http://repositorio.bc.ufg.br/tede/handle/tede/3895.
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This project, in the area of biomedical engineering, belongs to the promising field of research in surface electromyography (s-EMG). This technology can be used for in-depth study of some neuromuscular diseases, such as polyneuropathies and myopathies. Using an array of multichannel electrodes, we can also obtain the decomposition of s-EMG signals, estimation of conduction velocity of muscle fibers, location of innervation zones (set of motor units), among other applications. Although there are wireless electromyographers, there are no wireless electrode arrays in the market. Thinking about this, it was developed a wireless linear array of ten active electrodes for surface electromyography and a set of programs able to receive and process the data captured by this device. The hardware’s features are: low cost compared to similar equipment on the market, 12 bits resolution, 9216 samples per second (1024 samples per second per channel, with 9 channels and 10 electrodes in bipolar configuration), common mode rejection ratio greater than 50 dB; possess an interface for easy interaction with any computers via Bluetooth; enabling research in diverse areas (biomechanics, signal acquisition in athletes, animals, among other possibilities). In addition, it is powered by two lithium-ion batteries and autonomy of approximately 3 hours and 18 minutes. Although there were challenges in various stages of the device construction process, for example, in obtaining a high processing capacity and a high data transmission rate, the tests with prototypes show excellent results, consistent with the literature. After the implementation of the hardware, operational tests were performed as well as practical applications the use of a multi-channel electromyographer.
Esse projeto, da área da engenharia biomédica, pertence ao campo promissor de pesquisas em eletromiografia de superfície (EMG-s). Essa tecnologia pode ser usada para o estudo aprofundado de algumas doenças neuromusculares, como por exemplo, polineuropatias, miastenias e miopatias. Utilizando um arranjo de eletrodos multicanal, também podemos obter a decomposição de sinais de EMG-S, estimativa de velocidade de condução das fibras musculares, localização de zonas de inervação (conjunto de pontos motores), entre outras aplicações. Apesar de existirem eletromiógrafos sem fio, não há arranjos de eletrodos sem fio no mercado. Pensando nisso, foi desenvolvido um arranjo linear de dez eletrodos sem fio para eletromiografia de superfície e um conjunto de programas capazes de receber e processar os dados capturados por esse dispositivo. As características alcançadas por esse eletromiógrafo portátil são um baixo custo mesmo quando comparado aos eletromiógrafos de apenas um canal do mercado, 12 bits de resolução, 9216 amostras por segundo (1024 amostras por segundo por canal, com 9 canais e 10 eletrodos utilizando a configuração bipolar), taxa de rejeição de modo comum maior que 50 dB, uma interface que permite interação com computadores via Bluetooth, permitindo pesquisa em diversas áreas (biomecânica, aquisição de sinais em atletas, animais, entre outras possibilidades). Além disso, é alimentado por duas baterias de íon-lítio e possui uma autonomia média de 3 horas e 18 minutos. Apesar de terem surgidos desafios em várias etapas do processo de construção do dispositivo, como por exemplo, a obtenção de uma alta capacidade de processamento e de uma alta taxa de transmissão de dados, os testes com protótipos construídos mostram um resultado excelente e condizente com a literatura. Após a implementação deste hardware, foram realizados testes de funcionamento, assim como aplicações práticas da utilização de um eletromiógrafo de múltiplos canais.
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KO, MEI-JU, and 柯美如. "surface electromyography(sEMG) during swallowing from stroke patients with Dysphagia." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/29511219439007728916.
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碩士國立高雄師範大學
聽力學與語言治療研究所
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surface electromyography(sEMG) during swallowing from stroke patients with Dysphagia Abstract Dysphagia is a common complication in stroke patient. It not only impedes the quality of life but also increases the risk of pulmonary complications and even mortality. The videofluoroscopic swallowing study are take as the golden standard methods to assess dysphagia. However, it can’t be performed in the bedside. Our purpose is to investigate whether there is a difference of the sEMG during swallowing between normal population and stroke patients with dysphagia. After analyzing the signals, sEMG may be used as a quantifiable tools for dysphagia evaluation over the bedside. We obtained sEMG during swallowing, which consist of bilateral swallowing myoelectric signals, and compared the difference between stroke patients with dysphagia and normal population. We follow the method of “Vaiman(2007) sEMG swallowing evaluation process” when designing our study project. We recruited 20 stroke patients with dysphagia , and 20 normal subjects. Of all the participates, sEMG of four group of muscles(both sides) including obicularis oris,masseter,submental muscles and laryngeal strap muscles,during swallowing of 5 c.c. of water were recorded, Of the recorded sEMG, 7 variables such as baseline, average amplitude, peak amplitude, duration, peak latency, onset and offset relative to the orbicularis oris were analyzed. Independent t test were used to assess the inter-group difference. Results are as followed. 1. In stroke group, difference between sound side and hemi-side are significantly greater than those of normal group. The significant different variables contains: (1)Baseline, average amplitude and peak latency of orbicularis oris。 (2)Onset time of masseter and submental muscles groups。 (3)Average amplitude, peak amplitude and duration of laryngeal strap muscles。 2.When comparing the sound side and hemi-side of the stroke group, we found that except for the baseline of orbicularis oris at the sound side is higher than the hemi-side, there is no significant difference among the other parameters. Whereas, we can still see some trend of these parameters as followed. ＊The average amplitude of orbicularis oris, masseter, submental muscles group at the sound side are higher than hemi-side. Also, the duration of the sound side is longer than hemi-side. ＊The average amplitude and peak amplitude of laryngeal strap muscle group of the hemi-side is higher than sound side. Also, the duration of hemi-side is longer than the sound side. 3.When evaluating the relevant coefficient of all parameters and functional oral intake scale(FOIS), We found that only the average amplitude and peak amplitude of the masseter is significantly related with FOIS. Therefore, we concluded that sEMG recorded can only reflect how the swallowing muscles contract but still can’t be use to measure or interpret one’s functional oral intake ability. 28 swallowing electromyographic parameters have been analyzed and only 8 out of 28 (26％) shows significant difference. Among the parameters during pharyngeal phase of swallowing, the onset time of the submental muscles group in patient group is significantly greater than the normal group. Also, due to possible compensatory effects of laryngeal strap muscles group in stroke patients, it results in stronger power and longer contraction time over the hemi-side muscles than sound side. Eventually,it leads to the reason why the difference between hemi-side and sound-side in patient group is significantly smaller than the normal group. Due to the possible compensatory effect developed in stroke patient, the usage and interpretation of sEMG in assessing dysphagia. becomes too complicated and might be misleading. We concluded that in current acknowledge, it is not suitable to use sEMG for the evaluation of stroke patients with dysphagia. Whereas, the model of our study can still be further used to similar studies for different types of patients. key words: stroke、dysphagia、surface elecyromyography。
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Lim, Chin Guan, and 林進源. "MuscleSense: Sensing Workloads While Strength Training using Wearable Surface Electromyography (sEMG)." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/b85622.
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碩士國立臺灣大學
資訊工程學研究所
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Strength training improves overall health, well-being, physical appearance, and sports performance.There are four major factors that affect training efficacy in a training session: exercise type, number of repetitions, movement velocity, and workload. Prior research has used wearable sensors to detect exercise type, number of repetitions, and movement velocity while training. However, detecting workload still requires instrumentation of exercise equipment such as exercise machines, or free weights. This paper presents MuscleSense, an approach that detects training weight through wearable devices. In particular, MuscleSense uses various regressors to predicting weight using signals from wearable sEMG sensors mounted on user''s arm or forearm. We evaluated the effects of sensor placement and collected training data from 20 participants. The results from our user study show that MuscleSense achieves Root Mean Square Error(RMSE) of 0.683kg in sensing workload through sensors data from both forearm and arm using Support Vector Regressor of linear kernel.
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Zhang, Zhe. "Activity Intent Recognition of the Torso Based on Surface Electromyography and Inertial Measurement Units." 2013. https://scholarworks.umass.edu/theses/1098.
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This thesis presents an activity mode intent recognition approach for safe, robust and reliable control of powered backbone exoskeleton. The thesis presents the background and a concept for a powered backbone exoskeleton that would work in parallel with a user. The necessary prerequisites for the thesis are presented, including the collection and processing of surface electromyography signals and inertial sensor data to recognize the user’s activity. The development of activity mode intent recognizer was described based on decision tree classification in order to leverage its computational efficiency. The intent recognizer is a high-level supervisory controller that belongs to a three-level control structure for a powered backbone exoskeleton. The recognizer uses surface electromyography and inertial signals as the input and CART (classification and regression tree) as the classifier. The experimental results indicate that the recognizer can extract the user’s intent with minimal delay. The approach achieves a low recognition error rate and a user-unperceived latency by using sliding overlapped analysis window. The approach shows great potential for future implementation on a prototype backbone exoskeleton.
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Mountjoy, KATHERINE. "Use of a Hill-Based Muscle Model in the Fast Orthogonal Search Method to Estimate Wrist Force and Upper Arm Physiological Parameters." Thesis, 2008. http://hdl.handle.net/1974/1570.
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Modelling of human motion is used in a wide range of applications.
An important aspect of accurate representation of human movement is
the ability to customize models to account for individual
differences. The following work proposes a methodology using
Hill-based candidate functions in the Fast Orthogonal Search (FOS)
method to predict translational force at the wrist from flexion and
extension torque at the elbow. Within this force estimation
framework, it is possible to implicitly estimate subject-specific
physiological parameters of Hill-based models of upper arm muscles.
Surface EMG data from three muscles of the upper arm (biceps
brachii, brachioradialis and triceps brachii) were recorded from 10
subjects as they performed isometric contractions at varying elbow
joint angles. Estimated muscle activation level and joint kinematic
data (joint angle and angular velocity) were utilized as inputs to
the FOS model. The resulting wrist force estimations were found to
be more accurate for models utilizing Hill-based candidate
functions, than models utilizing candidate functions that were not
physiologically relevant. Subject-specific estimates of optimal
joint angle were determined via frequency analysis of the selected
FOS candidate functions. Subject-specific optimal joint angle
estimates demonstrated low variability and fell within the range of
angles presented in the literature.Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-10-30 01:32:01.606
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Láncz, Lukáš. "Strategie stabilizace postury při stoji na labilní ploše a při aplikaci válce s vodou." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-446887.
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Title: Strategy of postural stabilisation using unstable surface and water barrel. Objectives: The aim of this study is to investigate the level of muscle actiavtion of choosen muscles during lunge on unstable surface or with using water barrel. Investigation of the postural strategy used during lunge orn unstable surface or with the water barrel and creation of methodology for measurment and data analysis. Methods: Into this pilot study, there were picked 5 people (athletes). Data for outcomes where used from 3 athletes. The measurement of level of muscle activation were done by surface electromyography over gluteus medius muscle and musculi multifidii bilaterally.For data procession was used software Origin 2012 Postural stability was measured through force plates by Kistler and gained data were procesed by using software programmes Bioware, MS Excel and Matlab. For data analysis from EMG measurement was used simple comparasion of outcomes. Stabilometry outcomes were analysed by statistical methode t-test. Results: The results indicate greater level of measured trunk muscles activation during lunge with aquabag than lunge on unstable surface. Another thing which was found is that there was higher activation of Gluteus medius muscle on dominant lower extremity when performin lunge on unstable...
Books on the topic "Surface Electromyography (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 "Surface Electromyography (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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"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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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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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.
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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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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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Phinyomark, Angkoon, Franck Quaine, and Yann Laurillau. "The Relationship Between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface." In Computer Vision, 2234–68. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch098.
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Muscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition.
Conference papers on the topic "Surface Electromyography (sEMG)"
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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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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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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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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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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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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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Chiang, Joyce, Z. Jane Wang, and Martin J. McKeown. "Hidden Markov Multivariate Autoregressive (HMM-mAR) Modeling Framework for Surface Electromyography (sEMG) Data." In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4353420.
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Alam, A., M. Molter, A. Kapoor, B. Gaonkar, S. Benedict, L. Macyszyn, M. S. Joseph, and S. S. Iyer. "Flexible heterogeneously integrated low form factor wireless multi-channel surface electromyography (sEMG) device." In 2021 IEEE 71st Electronic Components and Technology Conference (ECTC). IEEE, 2021. http://dx.doi.org/10.1109/ectc32696.2021.00245.
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