Готові списки джерел за темами / Analysis of HD-sEMG signals

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Добірка наукової літератури з теми "Analysis of HD-sEMG signals"

Автор: Grafiati

Опубліковано: 7 вересня 2022

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Статті в журналах з теми "Analysis of HD-sEMG signals"

Al Harrach, M., S. Boudaoud, M. Hassan, F. S. Ayachi, D. Gamet, J. F. Grosset, and F. Marin. "Denoising of HD-sEMG signals using canonical correlation analysis." Medical & Biological Engineering & Computing 55, no. 3 (May 25, 2016): 375–88. http://dx.doi.org/10.1007/s11517-016-1521-x.

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Duan, Haiqiang, Chenyun Dai, and Wei Chen. "The Evaluation of Classifier Performance during Fitting Wrist and Finger Movement Task Based on Forearm HD-sEMG." Mathematical Problems in Engineering 2022 (March 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/9594521.

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The transmission of human body movement signals to other devices through wearable smart bracelets has attracted increasing attention in the field of human-machine interfaces. However, owing to the limited data collection range of wearable bracelets, it is necessary to study the relationship between the superposition of the wrist and fingers and their cooperative motions to simplify the data collection system of such devices. Multichannel high-density surface electromyogram (HD-sEMG) signals exhibit high spatial resolutions, and they can help improve the accuracy of the multichannel fitting. In this study, we quantified the HD-sEMG forearm spatial activation features of 256 channels of hand movement and performed a linear fitting of the data obtained for finger and wrist movements in order to verify the linear superposition relationship between the cooperative and independent movements of the wrist and fingers. This study aims to classify and predict the results of the fitting and measured fingers and wrist cooperative actions using four commonly adopted classifiers and evaluate the performance of the classifiers in gesture fitting. The results indicated that linear discriminant analysis affords the highest classification performance, whereas the random forest method achieved the worst performance. This study can serve as a guide for gesture signal simplification in the future.

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Veer, Karan. "Spectral and mathematical evaluation of electromyography signals for clinical use." International Journal of Biomathematics 09, no. 06 (August 2, 2016): 1650094. http://dx.doi.org/10.1142/s1793524516500947.

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The surface electromyography (SEMG) is a complicated biomedical signal, generated during voluntary or involuntary muscle activities and these muscle activities are always controlled by the nervous system. In this paper, the processing and analysis of SEMG signals at multiple muscle points for different operations were carried out. Myoelectric signals were detected using designed acquisition setup which consists of an instrumentation amplifier, filter circuit, an amplifier with gain adjustment. Further, Labview[Formula: see text]-based data programming code was used to record SEMG signals for independent activities. The whole system consists of bipolar noninvasive electrodes, signal acquisition protocols and signal conditioning at different levels. This work uses recorded SEMG signals generated by biceps and triceps muscles for four different arm activities. Feature extraction was done on the recorded signal for investigating the voluntary muscular contraction relationship for exercising statistic measured index method to evaluate distance between two independent groups by directly addressing the quality of signal in separability class for different arm movements. Thereafter repeated factorial analysis of variance technique was implemented to evaluate the effectiveness of processed signal. From these results, it demonstrates that the proposed method can be used as SEMG feature evaluation index.

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Zhang, Yanyan, Gang Wang, Chaolin Teng, Zhongjiang Sun, and Jue Wang. "The Analysis of Hand Movement Distinction Based on Relative Frequency Band Energy Method." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/781769.

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For the purpose of successfully developing a prosthetic control system, many attempts have been made to improve the classification accuracy of surface electromyographic (SEMG) signals. Nevertheless, the effective feature extraction is still a paramount challenge for the classification of SEMG signals. The relative frequency band energy (RFBE) method based on wavelet packet decomposition was proposed for the prosthetic pattern recognition of multichannel SEMG signals. Firstly, the wavelet packet energy of SEMG signals in each subspace was calculated by using wavelet packet decomposition and the RFBE of each frequency band was obtained by the wavelet packet energy. Then, the principal component analysis (PCA) and the Davies-Bouldin (DB) index were used to perform the feature selection. Lastly, the support vector machine (SVM) was applied for the classification of SEMG signals. Our results demonstrated that the RFBE approach was suitable for identifying different types of forearm movements. By comparing with other classification methods, the proposed method achieved higher classification accuracy in terms of the classification of SEMG signals.

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Wang, Gang, Yanyan Zhang, and Jue Wang. "The Analysis of Surface EMG Signals with the Wavelet-Based Correlation Dimension Method." Computational and Mathematical Methods in Medicine 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/284308.

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Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy.

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Herrera, Efrén V., Edgar M. Vela, Victor A. Arce, Katherine G. Molina, Nathaly S. Sánchez, Paúl J. Daza, Luis E. Herrera, and Douglas A. Plaza. "Temperature Influences at the Myoelectric Level in the Upper Extremities of the Human Body." Open Biomedical Engineering Journal 14, no. 1 (October 23, 2020): 28–42. http://dx.doi.org/10.2174/1874120702014010028.

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Objective: Nowadays, surface electromyography (sEMG) signals are used for a variety of medical interaction applications along with hardware and software interfaces. These signals require advanced techniques with different approaches that enable processing the sEMG signals acquired in the upper limb muscles of a person. Methods: The purpose of this article is to analyze the sEMG signals of the upper limb of a person exposed to temperature changes to envisage its behavior and its nature. The anticipated diagnostic is a key factor in the health field. Therefore, it is very important to develop more precise methods and techniques. For the present study, a heat chamber that allows controlling the temperature of the area where the patient rests his or her hand was designed and implemented. With the appropriate hardware interfaces, the sEMG signals of the hand were registered with MatLab/Simulink software for further analysis. The article explains the analysis and develops knowledge, through a probabilistic approach regarding the change in the sEMG signals. Results: The results show that there is an activity in the sEMG signal response due to changes in temperature and it is feasible to detect them using the proposed method. Conclusion: This finding contributes to research that seeks to characterize temperature’s effect in the biomedical field.

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Shahbakhti, Mohammad, Elnaz Heydari, and Gia Thien Luu. "Segmentation of ECG from Surface EMG Using DWT and EMD: A Comparison Study." Fluctuation and Noise Letters 13, no. 04 (October 20, 2014): 1450030. http://dx.doi.org/10.1142/s0219477514500308.

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The electrocardiographic (ECG) signal is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact from SEMG is investigated. The focus of this research is to reach the optimized number of decomposed levels using mean power frequency (MPF) by both techniques. In order to implement the proposed methods, ten simulated and three real ECG contaminated SEMG signals have been tested. Signal-to-noise ratio (SNR) and mean square error (MSE) between the filtered and the pure signals are applied as the performance indexes of this research. The obtained results suggest both techniques could remove ECG artifact from SEMG signals fair enough, however, DWT performs much better and faster in real data.

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Hari, Lakshmi M., Gopinath Venugopal, and Swaminathan Ramakrishnan. "Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 236, no. 2 (October 11, 2021): 208–17. http://dx.doi.org/10.1177/09544119211048011.

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In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

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Naik, Ganesh R., Dinesh K. Kumar, Sridhar P. Arjunan, and Marimuthu Palaniswami. "INDEPENDENT COMPONENT APPROACH TO THE ANALYSIS OF HAND GESTURE sEMG AND FACIAL sEMG." Biomedical Engineering: Applications, Basis and Communications 20, no. 02 (April 2008): 83–93. http://dx.doi.org/10.4015/s1016237208000647.

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Independent component analysis algorithm, a recently developed multivariate statistical data analysis technique, has been successfully used for signal extraction in the field of biomedical and statistical signal processing. This paper reviews the concept of ICA and demonstrates its usefulness and limitations in the context of surface electromyogram related to hand movements and facial muscles. In the first experiment, ICA has been used to separate the electrical activity from different hand gestures. The second part of our study considers separating electrical activity from facial muscles. In both instances, surface electromyogram has been used as an indicator of muscle activity. The theoretical analysis and experimental results demonstrate that ICA is suitable for the identification of different hand gestures using sEMG signals. The results identify the unsuitability of ICA when the similar techniques are used for the facial muscles in order to perform different vowel classification. This technique could be used as a prerequisite tool to measure the reliability of sEMG based systems in rehabilitations and human computer interaction applications.

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Lersviriyanantakul, Chaiwat, Apidet Booranawong, Kiattisak Sengchuai, Pornchai Phukpattaranont, Booncharoen Wongkittisuksa, and Nattha Jindapetch. "Implementation of a real-time automatic onset time detection for surface electromyography measurement systems using NI myRIO." Thermal Science 20, suppl. 2 (2016): 591–602. http://dx.doi.org/10.2298/tsci150929041l.

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For using surface electromyography (sEMG) in various applications, the process consists of three parts: an onset time detection for detecting the first point of movement signals, a feature extraction for extracting the signal attribution, and a feature classification for classifying the sEMG signals. The first and the most significant part that influences the accuracy of other parts is the onset time detection, particularly for automatic systems. In this paper, an automatic and simple algorithm for the real-time onset time detection is presented. There are two main processes in the proposed algorithm; a smoothing process for reducing the noise of the measured sEMG signals and an automatic threshold calculation process for determining the onset time. The results from the algorithm analysis demonstrate the performance of the proposed algorithm to detect the sEMG onset time in various smoothing-threshold equations. Our findings reveal that using a simple square integral (SSI) as the smoothing-threshold equation with the given sEMG signals gives the best performance for the onset time detection. Additionally, our proposed algorithm is also implemented on a real hardware platform, namely NI myRIO. Using the real-time simulated sEMG data, the experimental results guarantee that the proposed algorithm can properly detect the onset time in the real-time manner.

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Більше джерел

Дисертації з теми "Analysis of HD-sEMG signals"

Liu, Aiping. "FDR-controlled network modeling and analysis of fMRI and sEMG signals." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/37217.

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Neural recording technologies such as functional magnetic resonance imaging (fMRI) and surface electroencephalography (sEMG) provide great potential to studying the underlying neural systems and the related diseases. A broad range of statistical methods have been developed to model interactions between neural components. In this thesis, a false discovery rate (FDR)-controlled exploratory group modeling approach is introduced to model interaction/cooperation between neural components. Group network modeling for comparison between populations is of great common interest in biomedical signal processing, particularly when there might be considerable heterogeneity within one or more groups, such as disease populations. A group-level network modeling process, the group PCfdr algorithm with taking into account inter-subject variances, is proposed. The group PCfdr algorithm combines group inference with a graphical modeling approach for discovering statistically significant structure connectivity. Simulation results demonstrate that the group PCfdr algorithm can accurately recover the underlying group network structures and robustly control the FDR at user-specified levels. To further extract informative features and compare the connectivity patterns across groups at the network level, network analysis methods including graph theoretical analysis, lesion and perturbation analysis are applied to examine the inferred networks. It can provide great potential to investigate the connectivity patterns as well as the particular changes associated with certain disease states. The proposed network modeling and analysis approach is applied to fMRI data collected from control and Parkinson's Disease (PD) groups. The network analysis results of the PD groups before and after L-dopa medication support the hypothesis that PD subjects could be ameliorated by the medication. In addition, based on the comparison between PD subtypes, we observe that the learned brain effective networks across PD subtypes display different connectivity patterns. In another sEMG study in low back pain, significant findings of muscle coordination networks are found to be associated with low back pain. The results indicate that the networks representing the normal group clearly exhibit globally symmetrical patterns between the left and right sEMG channels, while the connections between sEMG channels for the patient group are more likely to cluster locally and the learned group networks show the loss of global symmetrical patterns.

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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 santé
With 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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Viljoen, Suretha. "Analysis of crosstalk signals in a cylindrical layered volume conductor influence of the anatomy, detection system and physical properties of the tissues /." Diss., Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-08082005-113739.

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Al, Harrach Mariam. "Modeling of the sEMG / Force relationship by data analysis of high resolution sensor network." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2298/document.

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Анотація:

Les systèmes neuromusculaires et musculo-squelettique sont considérés comme un système de systèmes complexe. En effet, le mouvement du corps humain est contrôlé par le système nerveux central par l'activation des cellules musculaires squelettiques. L'activation du muscle produit deux phénomènes différents : mécanique et électrique. Ces deux activités possèdent des propriétés différentes, mais l'activité mécanique ne peut avoir lieu sans l'activité électrique et réciproquement. L'activité mécanique de la contraction du muscle squelettique est responsable du mouvement. Le mouvement étant primordial pour la vie humaine, il est crucial de comprendre son fonctionnement et sa génération qui pourront aider à détecter des déficiences dans les systèmes neuromusculaire et musculo-squelettique. Ce mouvement est décrit par les forces musculaires et les moments agissant sur une articulation particulière. En conséquence, les systèmes neuromusculaires et musculo-squelettique peuvent être évalués avec le diagnostic et le management des maladies neurologiques et orthopédiques à travers l'estimation de la force. Néanmoins, la force produite par un seul muscle ne peut être mesurée que par une technique très invasive. C'est pour cela, que l'estimation de cette force reste l'un des grands challenges de la biomécanique. De plus, comme dit précédemment, l'activation musculaire possède aussi une réponse électrique qui est corrélée à la réponse mécanique. Cette résultante électrique est appelée l'électromyogramme (EMG) et peut être mesurée d'une façon non invasive à l'aide d'électrodes de surface. L'EMG est la somme des trains de potentiel d'action d'unité motrice qui sont responsable de la contraction musculaire et de la génération du mouvement. Ce signal électrique peut être mesuré par des électrodes à la surface de la peau et est appelé I'EMG de surface {sEMG). Pour un muscle unique, en supposant que la relation entre l'amplitude du sEMG et la force est monotone, plusieurs études ont essayé d'estimer cette force en développant des modèles actionnés par ce signal. Toutefois, ces modèles contiennent plusieurs limites à cause des hypothèses irréalistes par rapport à l'activation neurale. Dans cette thèse, nous proposons un nouveau modèle de relation sEMG/force en intégrant ce qu'on appelle le sEMG haute définition (HD-sEMG), qui est une nouvelle technique d'enregistrement des signaux sEMG ayant démontré une meilleure estimation de la force en surmontant le problème de la position de l'électrode sur le muscle. Ce modèle de relation sEMG/force sera développé dans un contexte sans fatigue pour des contractions isométriques, isotoniques et anisotoniques du Biceps Brachii (BB) lors une flexion isométrique de l'articulation du coude à 90°
The neuromuscular and musculoskeletal systems are complex System of Systems (SoS) that perfectly interact to provide motion. This interaction is illustrated by the muscular force, generated by muscle activation driven by the Central Nervous System (CNS) which pilots joint motion. The knowledge of the force level is highly important in biomechanical and clinical applications. However, the recording of the force produced by a unique muscle is impossible using noninvasive procedures. Therefore, it is necessary to develop a way to estimate it. The muscle activation also generates another electric phenomenon, measured at the skin using electrodes, namely the surface electromyogram (sEMG). ln the biomechanics literature, several models of the sEMG/force relationship are provided. They are principally used to command musculoskeletal models. However, these models suffer from several important limitations such lacks of physiological realism, personalization, and representability when using single sEMG channel input. ln this work, we propose to construct a model of the sEMG/force relationship for the Biceps Brachii (BB) based on the data analysis of a High Density sEMG (HD-sEMG) sensor network. For this purpose, we first have to prepare the data for the processing stage by denoising the sEMG signals and removing the parasite signals. Therefore, we propose a HD-sEMG denoising procedure based on Canonical Correlation Analysis (CCA) that removes two types of noise that degrade the sEMG signals and a source separation method that combines CCA and image segmentation in order to separate the electrical activities of the BB and the Brachialis (BR). Second, we have to extract the information from an 8 X 8 HD-sEMG electrode grid in order to form the input of the sEMG/force model Thusly, we investigated different parameters that describe muscle activation and can affect the relationship shape then we applied data fusion through an image segmentation algorithm. Finally, we proposed a new HDsEMG/force relationship, using simulated data from a realistic HD-sEMG generation model of the BB and a Twitch based model to estimate a specific force profile corresponding to a specific sEMG sensor network and muscle configuration. Then, we tested this new relationship in force estimation using both machine learning and analytical approaches. This study is motivated by the impossibility of obtaining the intrinsic force from one muscle in experimentation

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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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Анотація:

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 physique
The 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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Mishra, Ram Kinker. "Muscle Fatigue Analysis During Dyanamic Conraction." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2556.

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Анотація:

In the field of ergonomics, biomechanics, sports and rehabilitation muscle fatigue is regarded as an important aspect since muscle fatigue is considered to be one of the main reasons for musculoskeletal disorders. Classical signal processing techniques used to understand muscle behavior are mainly based on spectral based parameters estimation, and mostly applied during static contraction and the signal must be stationary within the analysis window; otherwise, the resulting spectrum will make little physical sense. Furthermore, the shape and size of the analysis window also directly affect the spectral estimation. But fatigue analysis in dynamic conditions is of utmost requirement because of its daily life applicability. It is really difficult to consistently find the muscle fatigue during dynamic contraction due to the inherent non-stationary nature and associated noise in the signal along with complex physiological changes in muscles. Nowadays, in addition to linear signal processing, different non-linear signal processing techniques are adopted to find out the consistent and robust indicator for muscle fatigue under dynamic condition considering the high degree of non-linearity (caused by functional interference between different muscles, changes of signal sources and paths to recording electrodes, variable electrode interface etc.) in the signal. In this work, various linear and nonlinear-non-stationary signal processing methods, applied on surface EMG signal for muscular fatigue analysis under dynamic contraction are studied. In present study, surface EMG (sEMG) signals are recorded from Biceps Brachii muscles from eight (N=8) physically active college students during dynamic lifting 7 kg load at the rate of 20 lifts/min till they become fatigue. EMG data is processed in two ways -1. taking the whole EMG response and 2. breaking into three ranges of contraction (0-45)o, (45-90)o and >90o, to study better response region. It is observed that in spectral estimation techniques auto-regressive (AR) based spectral estimation technique gives better frequency resolution than periodogram for small epochs, as AR is based on parametric estimation. Both the previous methods provide only the frequency information in the signal. In order to estimate the time varying nature of frequency content in a signal various time-frequency signal processing techniques are used like – Short Time-Fourier Transform (STFT), Smoothed pseudo Wigner-Ville (SPWD), Choi-William distribution (CWD), Continuous Wavelet Transform (CWT), Huang-Hilbert Transform (HHT) and Recurrence Quantification Analysis (RQA) are used. The last two techniques are used by considering the EMG signal as non-linear and non-stationary signals. Among these techniques, STFT is the simplest time-frequency analysis technique. But tradeoff between time and frequency resolution is the major constraint in STFT, therefore, a window length of 256 samples are considered in this study. In order to tackle time-frequency resolution problem different Cohen-class distribution techniques are used like SPWD and CWD, where the result is severely affected by the presence of interference terms which make its interpretation really difficult. Different adaptive filters are used in SPWD and CWD to suppress these interference terms during analysis. Among these time-frequency analysis techniques continuous wavelet transform provides the most accurate results in comparison to other time-frequency analysis techniques. Similar result is obtained in present study. This fatigue response is further improved using non-linear and non-stationary techniques like HHT and RQA. HHT shows less variation in frequency response than CWT analysis result. Percentage of determinism calculated using recurrence quantification analysis method is found to be more sensitive than mean frequency estimation. Therefore, non-linear and non-stationary signal processing techniques are to be better indicator of muscle fatigue during dynamic contraction.

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Частини книг з теми "Analysis of HD-sEMG signals"

Varghese, Aiswarya, K. B. Akshaya, S. Akshay Prakash, S. Sreehari, Divya Sasidharan, and G. Venugopal. "Analysis of Motorcycle Rider’s Posture Using sEMG Signals." In Lecture Notes in Electrical Engineering, 471–81. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0336-5_39.

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Banerjee, Swati, Loubna Imrani, Kiyoka Kinugawa, Jeremy Laforet, and Sofiane Boudaoud. "Analysis of HD-sEMG Signals Using Channel Clustering Based on Time Domain Features For Functional Assessment with Ageing." In Biomedical Engineering and Computational Intelligence, 83–92. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21726-6_8.

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Quizhpe-Cárdenas, Carlos, Francisco Ortiz-Ortiz, Freddy Bueno-Palomeque, and Marco Vinicio Vásquez Cabrera. "Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals." In Advances in Physical Ergonomics & Human Factors, 94–101. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94484-5_10.

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Chen, Chao, Farong Gao, Chunling Sun, and Qiuxuan Wu. "Muscle Synergy Analysis for Stand-Squat and Squat-Stand Tasks with sEMG Signals." In Biometric Recognition, 545–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97909-0_58.

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Strazza, Annachiara, Federica Verdini, Alessandro Mengarelli, Stefano Cardarelli, Andrea Tigrini, Sandro Fioretti, and Francesco Di Nardo. "Wavelet Analysis-Based Reconstruction for sEMG Signal Denoising." In IFMBE Proceedings, 245–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31635-8_29.

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Carriou, Vincent, Mariam Al Harrach, Jeremy Laforet, and Sofiane Boudaoud. "Sensitivity Analysis of HD-sEMG Amplitude Descriptors Relative to Grid Parameter Variation." In XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, 119–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32703-7_25.

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Shamli Fathima, P., C. Sandhra, Dolbin Jojo, A. V. Gayathri, N. Sidharth, and G. Venugopal. "Fatigue Analysis of Biceps Brachii Muscle Using sEMG Signal." In Lecture Notes in Electrical Engineering, 307–14. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0336-5_25.

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Ghiatt, Kawtar, Ahmad Diab, Sofiane Boudaoud, Kiyoka Kinugawa, John McPhee, and Ning Jiang. "Nonlinear Methods on HD-sEMG Signals for Aging Effect Evaluation During Isometric Contractions of the Biceps Brachii." In Intelligent Robotics and Applications, 354–62. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13841-6_33.

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Guerrero, F. N., P. A. García, and E. M. Spinelli. "Signal modes for design-oriented analysis of active sEMG spatial filter electrodes." In VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016, 504–7. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4086-3_127.

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Chang, Xin, Xinyi Li, Jian Li, Guihua Tian, Hongcai Shang, Jingbo Hu, Jiahao Shi, and Yue Lin. "Muscle Tension Analysis Based on sEMG Signal with Wearable Pulse Diagnosis Device." In Intelligent Robotics and Applications, 756–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89092-6_69.

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Тези доповідей конференцій з теми "Analysis of HD-sEMG signals"

Sebastian, Anish, Parmod Kumar, Marco P. Schoen, Alex Urfer, Jim Creelman, and D. Subbaram Naidu. "Analysis of EMG-Force Relation Using System Identification and Hammerstein-Wiener Models." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4185.

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Анотація:

Surface Electromyographic (sEMG) signals have been exploited for almost a century, for various clinical and engineering applications. One of the most compelling and altruistic applications being, control of prosthetic devices. The study conducted here looks at the modeling of the force and sEMG signals, using nonlinear Hammerstein-Weiner System Identification techniques. This study involved modeling of sEMG and corresponding force data to establish a relation which can mimic the actual force characteristics for a few particular hand motions. Analysis of the sEMG signals, obtained from specific Motor Unit locations corresponding to the index, middle and ring finger, and the force data led to the following deductions; a) Each motor unit location has to be treated as a separate system, (i.e. extrapolation of models for different fingers cannot be done) b) Fatigue influences the Hammerstein-Wiener model parameters and any control algorithm for implementing the force regimen will have to be adaptive in nature to compensate for the changes in the sEMG signal and c) The results also manifest the importance of the design of the experiments that need to be adopted to comprehensively model sEMG and force.

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Marri, Kiran, and Ramakrishnan Swaminathan. "Classification of Muscular Nonfatigue and Fatigue Conditions Using Surface EMG Signals and Fractal Algorithms." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9828.

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Анотація:

The application of surface electromyography (sEMG) technique for muscle fatigue studies is gaining importance in the field of clinical rehabilitation and sports medicine. These sEMG signals are highly nonstationary and exhibit scale-invariant self-similarity structure. The fractal analysis can estimate the scale invariance in the form of fractal dimension (FD) using monofractal (global single FD) or multifractal (local varying FD) algorithms. A comprehensive study of sEMG signal for muscle fatigue using both multifractal and monofractal FD features have not been established in the literature. In this work, an attempt has been made to differentiate sEMG signals recorded nonfatigue and fatigue conditions using monofractal and multifractal algorithms, and machine learning methods. For this purpose, sEMG signals have been recorded from biceps brachii muscles of fifty eight healthy subjects using a standard protocol. The signals of nonfatigue and fatigue region were subjected to eight monofractal (Higuchi, Katz, Petrosian, Sevcik, box counting, multi-resolution length, Hurst and power spectrum density) and two multifractal (detrended fluctuating and detrended moving average) algorithms and 28 FD features were extracted. The features were ranked using conventional and genetic algorithms, and a subset of FD features were further subjected to Naïve Bayes (NB), Logistic Regression (LR) and Multilayer Perceptron (MLP) classifiers. The results show that all fractal features are statistically significant. The classification accuracy using feature subset of conventional method is observed to be from 83% to 88%. The highest accuracy of 93.96% was achieved using genetic algorithm and LR classifier combination. The result demonstrated that the performance of multifractal FD features to be more suitable for sEMG signals as compared to monofractal FD features. The fractal analysis of sEMG signals appears to be a very promising biomarker for muscle fatigue classification and can be extended to detection of fatigue onset in varied neuromuscular conditions.

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El-Daydamony, Eman M., Mona El-Gayar, and Fatma Abou-Chadi. "A computerized system for SEMG signals analysis and classifieation." In 2008 National Radio Science conference (NRSC). IEEE, 2008. http://dx.doi.org/10.1109/nrsc.2008.4542388.

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Kumar, Parmod, Anish Sebastian, Chandrasekhar Potluri, Yimesker Yihun, Madhavi Anugolu, Jim Creelman, Alex Urfer, D. Subbaram Naidu, and Marco P. Schoen. "Spectral analysis of sEMG signals to investigate skeletal muscle fatigue." In 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011). IEEE, 2011. http://dx.doi.org/10.1109/cdc.2011.6161297.

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Contreras-Ortiz, Sonia H., and Luis A. Flórez-Prias. "Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection." In 13th International Symposium on Medical Information Processing and Analysis, edited by Jorge Brieva, Juan David García, Natasha Lepore, and Eduardo Romero. SPIE, 2017. http://dx.doi.org/10.1117/12.2285950.

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Worasawate, Raya Sakashita, Pined Laohapiengsak, and Muthita Wangkid. "CNN Classification of Finger Movements using Spectrum Analysis of sEMG Signals." In 2021 25th International Computer Science and Engineering Conference (ICSEC). IEEE, 2021. http://dx.doi.org/10.1109/icsec53205.2021.9684641.

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Shair, E. F., T. N. S. T. Zawawi, A. R. Abdullah, N. H. Shamsudin, and I. Halim. "sEMG signals analysis using time-frequency distribution for symmetric and asymmetric lifting." In 2015 International Symposium on Technology Management and Emerging Technologies (ISTMET). IEEE, 2015. http://dx.doi.org/10.1109/istmet.2015.7359035.

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Sarmiento, J. F., T. F. Bastos, A. B. Botti, A. Elias, A. Frizera, M. Hubner, and I. V. Silva. "Characterization and diagnosis of fibromyalgia based on fatigue analysis with sEMG signals." In 2012 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC). IEEE, 2012. http://dx.doi.org/10.1109/brc.2012.6222159.

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Soedirdjo, S. D. H., K. Ullah, and R. Merletti. "Power line interference attenuation in multi-channel sEMG signals: Algorithms and analysis." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7319227.

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Ghosh, Diptasree Maitra, and Ramakrishnan Swaminathan. "Fatigue Analysis in Biceps Brachii Muscles Using Semg Signals and Polynomial Chirplet Transform." In the 2017 International Conference. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3155077.3155090.

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