Tesi sul tema "Muscle EMG signal"

Les modifications des paramètres spectraux du signal électromyographique de surface (EMGS) des muscles Triceps Surae (T. S. ) et Tibialis Anterior (T. A. ), au cours d'une épreuve de fatigue isométrique, sont étudiées en relation avec une situation de microgravité simulée chez l'homme, c'est-à-dire lors d'une période de Bed Rest (B. R. ). La revue de la littérature a permis de montrer que : d'une part, lors d'une période de microgravité réelle ou simulée, les muscles à fonction antigravitaire (T. S) sont plus affectés que les muscles à fonction phasique (T. A. ) ; d'autre part, les paramètres spectraux EMGS évoluent différemment lors d'épreuves de fatigue et ceci en fonction de leurs caractéristiques métaboliques musculaires. L'étude a comporté deux phases principales : la première a consisté en la validation du protocole expérimental, la caractérisation des réponses des différents muscles en terme d'évolution des paramètres spectraux EMGS, et l'établissement d'une relation entre ces paramètres spectraux et certains paramètres du métabolisme musculaire exploré par spectroscopie RMN 31P ; la deuxième a été de caractériser l'évolution des paramètres spectraux EMGS en fonction du statut fonctionnel du T. S. Et du T. A. Lors d'une période de B. R. (4 semaines), avec et sans contre-mesures d'exercice musculaire. Les résultats montrent que : grâce à la méthode proposée (épreuve isométrique à 50% de la force maximale volontaire et analyse spectrale du signal EMGS), il est possible de différencier les évolutions des muscles en fonction de leur résistance à la fatigue grâce à l'établissement d'un débit de la fréquence moyenne (MPF) du spectre EMGS (% de diminution de la valeur initiale de la MPF par minute de temps de contraction). Ce débit constitue un index de fatigabilité d'un point de vue EMGS : il existe une relation entre le glissement spectral vers les basses fréquences de L'EMGS et la concentration musculaire en H2PO4 d'une part et H+ d'autre part ; il est possible de différencier ces évolutions par rapport à une situation de microgravité simulée, les différents chefs musculaires du T. S. (les gastrocnemii et le soleus) présentant une augmentation du débit de MPF contrairement au T. A. ; et enfin, lorsqu'un entraînement musculaire est non spécifique de la fonction des muscles étudiés, celui-ci n'est pas suffisant pour contrecarrer les effets du déconditionnement exprimés en terme EMGS. En conclusion, l'analyse spectrale du signal EMGS, lors d'épreuves de fatigue isométrique, apparaît comme étant un outil fiable pour discriminer les muscles par rapport à leur fonction antigravitaire (ou non) et en situation de microgravité simulée. L'aspect non invasif de cette méthode en fait une technique de choix pour le suivi de l'adaptation du muscle dans les domaines de la physiologie spatiale, sportive et de la médecine

Manufacturing and construction workers undertake physically strenuous activities increasing the risk of health problems, disability, and sick leave, leading to lower job attractiveness and job candidate scarcity. In the EU, up to 44 million workers are affected by workplace-related musculoskeletal disorders (MSDs), representing a total annual cost of more than €240 billion. Exoskeleton use could alleviate muscle peak loads and reduce the risks of injury of workers. This work is related to the INTERREG's project "EXSCALLERATE" which aimed to accelerate the adoption of exoskeletons among SMEs. This research presents a comparative study of using exoskeletons by workers while performing different tasks related to their job. The tests evaluate the advantages of using exoskeletons in reducing human muscle activity, thereby, reducing the fatigue and tiredness. The study uses two commercially available exoskeletons, (1) upper body exoskeleton known as Eksovest and (2) lower body exoskeleton known as LegX. For upper body, the study performed drilling tasks at shoulder height and roof drilling positions, whereas, for the lower body, virtual chair position and squatting positions are tested which involved frequent bending of knees. Besides, the experiments based on accuracies of the data collection techniques and compare three volunteer’s body muscle data acquired by EMG sensor. From these comparisons, it is found that the muscle activity can be reduced up to 60% by using these exoskeletons, hence, increasing the work life of the workforce. The results of this study will help create awareness among SMEs towards the adoption of exoskeletons.

During muscle contraction, electrical signals are generated by the muscle cells. The analysis of those signals is called electromyography (EMG). The EMG signal is mainly determined by physiological factors including so called central factors (central nervous system origin) and peripheral factors (muscle tissue origin). In addition, during the acquisition of EMG signals, technical factors are introduced (measurement equipment origin). The aim of this dissertation was to develop and evaluate methods to estimate physiological properties of the muscles using multichannel surface EMG (MCsEMG) signals. In order to obtain accurate physiological estimates, a method for automatic signal quality estimation was developed. The method’s performance was evaluated using visually classified signals, and the results demonstrated high classification accuracy. A method for estimation of the muscle fibre conduction velocity (MFCV) and the muscle fibre orientation (MFO) was developed. The method was evaluated with synthetic signals and demonstrated high estimation precision at low contraction levels. In order to discriminate between the estimates of MFCV and MFO belonging to single or populations of motor units (MUs), density regions of so called spatial distributions were examined. This method was applied in a study of the trapezius muscle and demonstrated spatial separation of MFCV (as well as MFO) even at high contraction levels. In addition, a method for quantification of MU synchronisation was developed. The performance on synthetic sEMG signals showed high sensitivity on MU synchronisation and robustness to changes in MFCV. The method was applied in a study of the biceps brachii muscle and the relation to force tremor during fatigue. The results showed that MU synchronisation accounted for about 40 % of the force tremor. In conclusion, new sEMG methods were developed to study muscle function and motor control in terms of muscle architecture, muscle fibre characteristics, and processes within the central nervous system.

Cette thèse traite de la problématique de la détection de rupture dans le cadre séquentiel où le signal est supposé être observé en temps réel et le phénomène passe de son état de départ "normal" à un état post-changement "anormal". Le défi de la détection séquentielle est de minimiser le délai de détection, soumis à une limite tolérable de fausse alarme. L'idée est de tester séquentiellement l'existence d'une rupture par l'écriture récursive de la statistique de détection en fonction du score, qui remplace le Log-Likelihood Ratio lorsque la distribution des données est inconnue. La procédure de détection repose ainsi sur une statistique récursive, un seuil de détection et une règle d'arrêt. Dans un premier travail, nous considérons la statistique score-CUSUM et proposons d'évaluer la performance de détection de certains seuils de détection. Deux seuils sont issus de la littérature, et trois nouveaux seuils sont construits par une méthode basée sur la simulation: le premier est constant, le second instantané et le troisième est une version dynamique "data-driven" du précédent. Nous définissons rigoureusement chacun des seuils en mettant en évidence les différentes notions du risque de fausse alarme contrôlé suivant le seuil. Par ailleurs, nous proposons une nouvelle règle d'arrêt corrigée pour réduire le taux de fausse alarme. Nous effectuons ensuite une étude de simulation pour comparer les différents seuils et évaluer la règle d'arrêt corrigée. Nous constatons que le seuil empirique conditionnel est le meilleur pour minimiser le délai de détection tout en maintenant le risque toléré de fausse alarme. Cependant, sur des données réelles, nous recommandons d'utiliser le seuil data-driven car c'est le plus simple à construire et à utiliser pour une implémentation pratique. Dans la seconde partie, nous appliquons notre méthodologie de détection data-driven sur des signaux physiologiques, à savoir des signaux temporels enregistrés au niveau du faisceau supérieur du trapèze de 30 sujets effectuant différentes activités bureautiques. La méthodologie est sujet-activité dépendante; elle inclut l'estimation on-line des paramètres du signal et la construction du seuil data-driven sur le début du signal de chaque activité de chaque sujet. L'objectif était d'identifier des changements de régimes au cours d'une activité afin d'évaluer le niveau de sollicitation du muscle et la variabilité du signal EMG, qui sont liés à la fatigue musculaire. Les résultats obtenus ont confirmé l'aisance de notre méthodologie et la performance et praticité du seuil data-driven proposé. Par la suite, les résultats ont permis la caractérisation de chaque type d'activité en utilisant des modèles mixtes
This thesis deals the problem of change-point detection in the sequential framework where the signal is assumed to be observed in real time and the phenomenon changes from its "normal" starting state to an "abnormal" post-change state. The challenge of sequential detection is to minimize the detection delay, subject to a tolerable false alarm limit. The idea is to sequentially test for the existence of a change-point by recursively writing the detection statistic as a function of the score, which replaces the Log-Likelihood Ratio when the data distribution is unknown. The detection procedure is thus based on a recursive statistic, a detection threshold and a stopping rule. In a first work, we consider the score-CUSUM statistic and propose to evaluate the detection performance of some detection thresholds. Two thresholds come from the literature, and three new thresholds are constructed by a method based on simulation: the first is constant, the second instantaneous and the third is a dynamic "data-driven" version of the previous one. We rigorously define each of the thresholds by highlighting the different notions of the controlled false alarm risk according to the threshold. Moreover, we propose a new corrected stopping rule to reduce the false alarm rate. We then perform a simulation study to compare the different thresholds and evaluate the corrected stopping rule. We find that the conditional empirical threshold is the best to minimize the detection delay while maintaining the tolerated risk of false alarms. However, on real data, we recommend using the data-driven threshold as it is the easiest to build and use for practical implementation. In the second part, we apply our data-driven detection methodology to physiological signals, namely temporal signals recorded at the level of the upper trapezium beam of 30 subjects performing different office activities. The methodology is subject-activity dependent; it includes the on-line estimation of the signal parameters and the construction of the data-driven threshold on the start of the signal of each activity of each subject. The objective was to identify regime changes during an activity in order to assess the level of muscle solicitation and EMG signal variability, which are associated with muscle fatigue. The results obtained confirmed the ease of our methodology and the performance and practicality of the proposed data-driven threshold. Subsequently, the results allowed the characterization of each type of activity using mixed models

Motor Unit (MU) innervation zones (IZs) localization is an important step in several clinical and non-clinical applications including 1) Acquisition of sEMG signal for accurate estimation of its amplitude and other parameters by avoiding placing the electrodes on IZs, 2) Accurate estimation of the EMG-Force relationship, 3) Effective injection of Botulinum Toxin in Post-stroke Spasticity near the IZs, and 4) Guiding the obstetricians to perform episiotomy during child delivery by avoiding cutting near the IZs of External Anal Sphincter (EAS) muscle. The minimal invasive way to identify the location of the IZs generally for any muscle and specifically for EAS muscle is to use multi-channel EMG signals. MU IZs can be detected from the multi-channel sEMG signals, for a fusiform muscle if the signal is acquired with an array of electrodes placed parallel to the muscle fibers, using digital signal and image processing algorithms. As most of the signal processing algorithms work on an adequate quality of the signal, thus before detecting the innervation zone it is made sure that the signal is of good quality. For this purpose, a method based on statistical thresholding of various parameters is proposed to detect the bad channels in the sEMG signals. If the number of the bad consecutive channels are more than 2 then it is suggested to acquire the signal again, otherwise each bad channel is approximated by the interpolation of its neighbor channels. As some background noise is always acquired with the EMG signal so further image enhancement techniques are used to enhance the MUAP propagation region in the spatio-temporal images and suppress the background noise. The MUAP pattern is then detected in the spatio-temporal sEMG images using multi-scale Hessian based filtering and the corresponding MU IZs are identified as the starting point of propagation of the MUAP. A software is also developed which can be used to visualize the signals acquired from EAS, detect and display the IZs and more importantly compute and display the histogram of the IZs and generate reports which will help the obstetrician while performing episiotomy during child delivery to avoid cutting vulnerable regions that may lead to fecal incontinence at later age.

Los movimientos voluntarios del cuerpo son controlados por el sistema nervioso central y periférico a través de la contracción de los músculos esqueléticos. La contracción se inicia al liberarse un neurotransmisor sobre la unión neuromuscular, iniciando la propagación de un biopotencial sobre la membrana de las fibras musculares que se desplaza hacia los tendones: el Potencial de Acción de la Unidad Motora (MUAP). La señal electromiográfica de superficie registra la activación continua de dichos potenciales sobre la superficie de la piel y constituye una valiosa herramienta para la investigación, diagnóstico y seguimiento clínico de trastornos musculares, así como para la identificación de la intención movimiento tanto en términos de dirección como de potencia. En el estudio de las enfermedades del sistema neuromuscular es necesario analizar el nivel de actividad, la capacidad de producción de fuerza, la activación muscular conjunta y la predisposición a la fatiga muscular, todos ellos asociados con factores fisiológicos que determinan la resultante contracción mioeléctrica. Además, el uso de matrices de electrodos facilita la investigación de las propiedades periféricas de las unidades motoras activas, las características anatómicas del músculo y los cambios espaciales en su activación, ocasionados por el tipo de tarea motora o la potencia de la misma. El objetivo principal de esta tesis es el diseño e implementación de protocolos experimentales y algoritmos de procesado para extraer información fiable de señales sEMG multicanal en 1 y 2 dimensiones del espacio. Dicha información ha sido interpretada y relacionada con dos patologías específicas de la extremidad superior: Epicondilitis Lateral y Lesión de Esfuerzo Repetitivo. También fue utilizada para identificar la dirección de movimiento y la fuerza asociada a la contracción muscular, cuyos patrones podrían ser de utilidad en aplicaciones donde la señal electromiográfica se utilice para controlar interfaces hombre-máquina como es el caso de terapia física basada en robots, entornos virtuales de rehabilitación o realimentación de la actividad muscular. En resumen, las aportaciones más relevantes de esta tesis son: * La definición de protocolos experimentales orientados al registro de señales sEMG en una región óptima del músculo. * Definición de índices asociados a la co-activación de diferentes músculos * Identificación de señales artefactuadas en registros multicanal * Selección de los canales mas relevantes para el análisis  Extracción de un conjunto de características que permita una alta exactitud en la identificación de tareas motoras Los protocolos experimentales y los índices propuestos permitieron establecer que diversos desequilibrios entre músculos extrínsecos del antebrazo podrían desempeñar un papel clave en la fisiopatología de la epicondilitis lateral. Los resultados fueron consistentes en diferentes ejercicios y pueden definir un marco de evaluación para el seguimiento y evaluación de pacientes en programas de rehabilitación motora. Por otra parte, se encontró que las características asociadas con la distribución espacial de los MUAPs mejoran la exactitud en la identificación de la intención de movimiento. Lo que es más, las características extraídas de registros sEMG de alta densidad son más robustas que las extraídas de señales bipolares simples, no sólo por la redundancia de contacto implicada en HD-EMG, sino también porque permite monitorizar las regiones del músculo donde la amplitud de la señal es máxima y que varían con el tipo de ejercicio, permitiendo así una mejor estimación de la activación muscular mediante el análisis de los canales mas relevantes.
Voluntary movements are achieved by the contraction of skeletal muscles controlled by the Central and Peripheral Nervous system. The contraction is initiated by the release of a neurotransmitter that promotes a reaction in the walls of the muscular fiber, producing a biopotential known as Motor Unit Action Potential (MUAP) that travels from the neuromuscular junction to the tendons. The surface electromyographic signal records the continuous activation of such potentials over the surface of the skin and constitutes a valuable tool for the diagnosis, monitoring and clinical research of muscular disorders as well as to infer motion intention not only regarding the direction of the movement but also its power. In the study of diseases of the neuromuscular system it is necessary to analyze the level of activity, the capacity of production of strength, the load-sharing between muscles and the probably predisposition to muscular fatigue, all of them associated with physiological factors determining the resultant muscular contraction. Moreover, the use of electrode arrays facilitate the investigation of the peripheral properties of the active Motor Units, the anatomical characteristics of the muscle and the spatial changes induced in their activation of as product of type of movement or power of the contraction.The main objective of this thesis was the design and implementation of experimental protocols, and algorithms to extract information from multichannel sEMG signals in 1 and 2 dimensions of the space. Such information was interpreted and related to pathological events associated to two upper-limb conditions: Lateral Epicondylitis and Repetitive Strain Injury. It was also used to identify the direction of movement and contraction strength which could be useful in applications concerning the use of biofeedback from EMG like in robotic- aided therapies and computer-based rehabilitation training.In summary, the most relevant contributions are:§The definition of experimental protocols intended to find optimal regions for the recording of sEMG signals. §The definition of indices associated to the co- activation of different muscles. §The detection of low-quality signals in multichannel sEMG recordings.§ The selection of the most relevant EMG channels for the analysis§The extraction of a set of features that led to high classification accuracy in the identification of tasks.The experimental protocols and the proposed indices allowed establishing that imbalances between extrinsic muscles of the forearm could play a key role in the pathophysiology of lateral epicondylalgia. Results were consistent in different types of motor task and may define an assessment framework for the monitoring and evaluation of patients during rehabilitation programs.On the other hand, it was found that features associated with the spatial distribution of the MUAPs improve the accuracy of the identification of motion intention. What is more, features extracted from high density EMG recordings are more robust not only because it implies contact redundancy but also because it allows the tracking of (task changing) skin surface areas where EMG amplitude is maximal and a better estimation of muscle activity by the proper selection of the most significant channels.

Le système nerveux central contrôle des mouvements par l’activation des unités de motrices (UM), les plus petites structures fonctionnelles du muscle. Les UM produisent une activité électrique qui peut être détectée par la technique de l’électromyographie de surface (EMGs). Le caractère stochastique du signal EMGs est dû principalement à la superposition des trains de potentiels d’action d’UM (TPAUM) (recrutement spatial), les TPAUM sont caractérisés par leurs instants de décharge (recrutement temporel), ainsi que par la forme des potentiels d’action (PA), qui dépend de certains facteurs méthodologiques et de facteurs intrinsèques au muscle. Le but de cette thèse sera d’étudier les possibilités et les limites d’utilisation de l’analyse de forme de la densité de probabilité des amplitudes (DP) du signal EMGs comme indicateur sur les stratégies de recrutement des UM et du contrôle moteur. Cette analyse semble pertinente puisque le signal EMGs est la somme de processus aléatoires ; les TPAUM. Des modifications sur ces variables devraient être perçues sur le signal composite. La contribution apportée par cette thèse se scinde en deux parties : la proposition d’un modèle complet de génération qui s’inspire de travaux récents issus de la littérature. Ce modèle prend en considération, pour la génération du signal EMGs, de nombreux paramètres physiologiques, anatomiques et nerveux, ainsi que la génération de la force. Cette prise en compte permet d’avoir un meilleur réalisme lors de la simulation. La deuxième partie concerne plusieurs études, en simulation et en expérimental, sur l’analyse des signaux EMGs monopolaires détectés sur le biceps brachial lors de contractions isométriques isotonique (force constante)/anisotonique (force graduée). L’objectif est d’extraire de l’information sur le patron de recrutement des UM à partir de ces signaux. Dans ce contexte, nous avons testé deux approches d’analyse de forme de la DP du signal EMGs qui sont la Statistique d’Ordre Supérieur (SOS), et un algorithme récent, le modèle de forme noyau (CSM : Core Shape Modeling). Les résultats indiquent une forte sensibilité des descripteurs proposés pour la séparation des classes de signaux (force, niveau de synchronisation de décharge), à l’effet filtrant du tissu adipeux et de la composante non propagée. L’efficacité de la classification dépend d’autre part de l’anatomie et du nombre d’UM composant le muscle. Pour les facteurs neuronaux, les deux stratégies de recrutement testées donnent les mêmes tendances avec plus de réalisme physiologique pour l’une d’entre elles. De plus, l’analyse de forme (par SOS), dans certains cas, nous donne des informations sur l’anatomie du muscle considéré, en termes de position de l’UM par rapport à l’électrode. En termes de performance de classification, l’algorithme CSM, donne un résultat relativement meilleur que l’approche SOS, que ce soit en simulation ou en expérimentation. Pour résumer, ce travail de thèse s’inscrit comme une démarche exploratoire du potentiel de l’analyse de forme de la DP du signal EMGs dans l’extraction d’information sur les modalités d’activation musculaire. De nombreux efforts restent à fournir en accord avec les perspectives proposées
The central nervous system control the movement through the activation of the motors units (MUs), the smallest muscle functional structure. The MU produce electrical activity that can be detected by the technique of surface electromyography (sEMG). The stochastic nature of EMGs signal is mainly due to the superposition of trains of MU action potentials ( MUAPT) (spatial recruitment), the MUAPT are characterized by their discharge frequency (temporal recruitment) and the shape of the action potential (PA), which depends on some factors methodological and intrinsic to the muscle. The aim of this thesis is to study the possibilities and limitations of using the shape analysis of the EMGs signal’s probability density function (DP) as an indicator on MU recruitment strategies and motor control. This analysis seems relevant since the EMGs signal is the sum of random processes, the MUAPT. The contribution of this thesis is divided into two parts : the proposal of a complete model generation inspired by recent work from the literature. This model takes into consideration, for the EMGs signal generation, many physiological, anatomical and nervous parameters, as well as the force generation. Such consideration allows for greater realism in the simulation. The second part concerns several studies, simulation and experimental analysis of EMGs monopolar signals detected on the biceps brachii during isometric contractions isotonic (constant force) / anisotonique (graduated force). The aim is to extract information on the pattern of MU recruitment from these signals. In this context, we tested two approaches based on the shape analysis of the EMGs signal’s DP which are the Higher Order Statistics (HOS), and a recent algorithm, the Core Shape Modeling (CSM). The results indicate a high sensitivity of the proposed descriptors for separating classes of signals (force, sync level of the discharge), the filtering effect of adipose tissue and non propagating component. The efficiency of the classification depends the other hand of the anatomy and the number of MU which composed the muscle. For neuronal factors, both recruitment strategies tested give similar trends with one of them is physiologically more realistic. In addition, analysis of shape (SOS), in some cases, gives us information about muscle anatomy of the concerned muscle, in terms of MU position relative to the electrode. Concerning performance of classification, the algorithm CSM gives a result relatively better than SOS approach, either in simulation or experimentation. To summarize, this thesis is listed as an exploratory process of the shape analysis potential of the EMGs signal’s DP in order to extract the information on the muscular activation’s modalities. A lot of efforts are still required in accordance with the perspectives offered

L’estimation de la force générée par un muscle est importante dans les études biomécaniques et pour les applications cliniques. Puisque cette force ne peut pas être mesurée directement, le signal électromyographique de surface (SEMG), reflétant le niveau d’activation musculaire, est utilisé pour quantifier la force développée. Cependant, tous les facteurs, contrôlant une contraction isométrique, n’influencent pas la force et le SEMG simultanément. Le but de ce travail de thèse est donc de développer un modèle de simulation conjointe du SEMG et de la force, afin d’étudier la relation EMG-force. Dans ce but, nous avons d’abord développé une nouvelle méthode de simulation de la force musculaire à partir d’un modèle d’EMG existant. Le modèle complet a été testé pour le choix de la stratégie de recrutement et l’influence de la durée de la consigne. Puis, nous avons utilisé une méthode de Monte Carlo pour étudier la sensibilité du modèle aux différents paramètres physiologiques d’entrée. Deux critères existants (relations EMG-force et force-variabilité de force) ainsi qu’un nouveau critère (erreur entre la consigne de force et la force générée), ont été utilisés pour optimiser les paramètres avec une consigne de force constante. Ce nouveau critère a ensuite été utilisé avec une consigne de force variable (sinusoïdale ou triangulaire), afin d’obtenir les plages optimales des paramètres. Enfin, pour évaluer notre modèle, nous avons réalisés des expérimentations et une simulation pour le biceps. Les résultats montrent que notre modèle EMG-force est capable de simuler qualitativement les comportements réels du biceps pour les contractions isotoniques et anisotoniques
The estimation of the force generated by a muscle is important in biomechanical studies and clinical applications. As this force cannot be measured directly, the surface electromyography signal (SEMG), reflecting the level of muscle activation, is used to quantify the force developed. However, all the factors controlling an isometric contraction do not influence the force and the SEMG simultaneously. The aim of this study is to develop a simulation model of SEMG and force in order to study the EMG-force relationship. For this purpose, we first developed a new method to simulate the muscle force from an existing EMG model. We tested the complete model with two recruitment strategies and studied the influence of target force duration. Then we used a Monte Carlo method to study the sensitivity of the model to various input physiological parameters. Two existing criteria (EMG-force and force-force variability relationships) and a new criterion (error between the target force and the generated force) were used to optimize the parameters in constant target force contractions. This new criterion was then used in variable target force contractions (sinusoidal or triangular target) in order to obtain the optimum parameter ranges. Finally, to evaluate our model, we performed experiments and simulations for the biceps. The results have shown that our EMG-force model can qualitatively simulate the behaviour of the biceps for isotonic and anisotonic contractions

The main goal of this thesis was to discover the relationships between MU characteristics and MUP features. To reach this goal, several features explaining the anatomical structure of the muscle were introduced. Additionally, features representing specific properties of the EMG signal detected from that muscle, were defined. Since information regarding the underlying anatomy was not available from real data, a physiologically based muscle model was used to extract the required features. This muscle model stands out from others, by providing similar acquisition schemes as the ones utilized by physicians in real clinical settings and by modelling the interactions among different volume conductor factors and the collection of MUs in the muscle in a realistic way. Having the features ready, several relationship discovery techniques were used, to reveal relationships between MU features and MUP features. To interpret the results obtained from the correlation analysis and pattern discovery techniques properly, several algorithms and new statistics were defined. The results obtained from correlation analysis and pattern discovery technique were similar to each other, and suggested that to maximize the inter-relationships between MUP features and MU features, MUPs could be filtered based on their slope values, specifically MUPs with slopes lower than 0. 6 v/s could be excluded. Additionally PDT results showed that high slope MUPs were not as informative about the underlying MU and could be excluded to maximize the relationships between MUP features and MU characteristics. Certain MUP features were determined to be highly related to certain MU characteristics. MUP area and duration were shown to be the best representative feature for the MU size and average fiber density, respectively. For the distribution of fiber diameter in the MU, duration and number of turns were determined to reflect mean fiber diameter and stdv of fiber diameter the best, correspondingly.

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

The aim of this thesis is to determine whether surface electromyography (EMG) can be used as a diagnostic tool in chiropractic practice to identify the functional status of the lumbar paraspinal muscles. There were two main studies to achieve this aim. The reliability and validity of the surface EMG signal to measure the activity of paraspinal muscles during maintenance of simple static postures was evaluated. During maintenance of static postures, the raw surface EMG signal was often contaminated by an electrocardiographic (ECG) signal. Although the ECG artefact was successfully removed using two different ECG removal techniques (manual and semi-automatic), the reliability of the surface EMG signal was not significantly improved (ICC less than 0.75) for both non-normalised and normalised data. Therefore the static postures that were used in this thesis did not provide a protocol that can be used to measure the functional status of the lumbar paraspinal muscles in clinical practice. However, when muscle contraction was at a moderate level, the reliability of EMG signal became better. Walking was considered to be a possible protocol to record a reliable surface EMG signal from paraspinal muscles. Three components of the surface EMG signal were used to characterise the pattern of muscle activity during steady state walking. The narrow window technique was used to characterise the peak activation point of the activity envelope in order to capture a stationary signal from which to calculate amplitude and frequency measures. Walking is a cyclic activity. The back muscles contract rhythmically during a single gait cycle. It is possible to identify the start and end points of the activity envelope associated with the rhythmic contraction of the muscles and define the timing of the muscle activation cycle relative to heel strike. The metronome was found to be useful to control the pace of natural walking in this study. The surface EMG signal of the first recording minute (1 ~ 2 minute) was not associated with a signal that was stable in terms of the parameters that were used in this study. It wa s found that the last recording minute (9 ~ 10 minute) can be used. This suggests that it may be necessary for subjects to walk for a defined period lasting some minutes before the commencement of recording of the surface EMG. Surface EMG may be used as a tool to measure activation patterns of the low back muscles during muscle contraction associated with the support of various static postures or during the execution of dynamic movements such as walking in the real world. The static postures used in this thesis to record the surface EMG signal from the lumbar paraspinal muscles were found not to provide the basis for a reliable and valid tool. However, a walking exercise might be an alternative activity which can be used easily in clinical practice. The components of the surface EMG signal that may be used in future studies might include measures of the amplitude, frequency and timing of the surface EMG signal.

L’utilisation du signal électrique musculaire de surface (EMG) dans une perspective biomécanique, thérapeutique ou pour la commande nécessite une forte sélectivité spatiale des signaux. Pour des muscles contigus, cette contrainte est rarement observée rendant l’utilisation du signal EMG difficile. La diaphonie, ou contamination croisée des signaux, inhérentes aux enregistrements doit alors être supprimée.Cette thèse a pour but de proposer des méthodes pour séparer la diaphonie lorsque les muscles extenseurs de l'index et du petit doigt sont en contraction simultanée. Notre travail consiste alors à extraire l’activité musculaire liée à chaque muscle dans un contexte de séparation de sources. Pour cela, une première partie du travail a consisté à élaborer une base de données, de qualité et exploitable, en enregistrant de manière non invasive les signaux EMG à partir de matrices d’électrodes, et à la mettre en forme pour la mettre à disposition de la communauté scientifique. Dans un second temps, diverses approches de traitement du signal ont été mise en œuvre pour réduire la diaphonie. Au final, nous proposons une méthode basée sur la décomposition tensorielle non négative de type PARAFAC2 appliquée aux enveloppes des signaux EMG obtenues à partir de la RMS sur des fenêtres glissantes afin de séparer l’activité de chaque muscle. L’originalité du modèle proposé repose sur l’ajout de deux contraintes principales en plus de celles relatives à PARAFAC2. La première contrainte est liée à la physiologie musculaire et implique la continuité spatiale des cartes d’acquisition, tandis que la seconde contrainte est relative à notre protocole expérimental et introduit de la parcimonie. Le modèle a été testé et validé sur des signaux réels puis sur des mélanges artificiels de signaux réels. La méthode proposée offre de meilleures performances de séparation par rapport à l’algorithme NN-PARAFAC2 et plus généralement par rapport à l’ensemble des autres méthodes de séparation de sources classiquement utilisées. Les limites et perspectives sont envisagées dans la dernière partie du document
The use of surface electromyographic (EMG) signals in a biomechanical, therapeutic, or control perspective requires a high spatial selectivity of the signals. In the case of adjacent muscles, this constraint is rarely met, making EMG signal utilization challenging. Crosstalk, or signal contamination inherent in recordings, must be eliminated.This thesis aims to propose methods for separating crosstalk when the extensor muscles of the index and little finger contract simultaneously. Our work focuses on extracting the muscle activity associated with each muscle in a source separation context. To achieve this, the initial part of the work involved creating a high-quality and usable database by non-invasively recording EMG signals from electrode arrays and formatting it for the scientific community's use. In the next phase, various signal processing approaches were employed to reduce crosstalk. Ultimately, we present a method based on non-negative tensor decomposition of the PARAFAC2 type applied to the envelopes of EMG signals obtained through root mean square (RMS) on sliding windows to separate the activity of each muscle. The uniqueness of the proposed model lies in the addition of two primary constraints in addition to those associated with PARAFAC2. The first constraint is related to muscle physiology and involves spatial continuity in the acquisition maps, while the second constraint is specific to our experimental protocol and introduces sparsity.The model was tested and validated on real signals and artificial mixtures of real signals. The proposed method demonstrates superior separation performance compared to the NN-PARAFAC2 algorithm and, more broadly, relative to conventional source separation methods. The document concludes by discussing its limitations and potential future directions

L’objectif de notre recherche est de comprendre les bases cérébrales de l’interaction sociale dans un contexte de performance musicale grâce à des outils issus des neurosciences (électroencéphalographie : EEG) et du traitement du signal. Ce manuscrit présente tout d’abord un état de l’art des études récentes dans le domaine de l’hyperscanning. Nous offrons une réflexion sur les prérequis et la méthodologie à adopter pour concevoir une expérience prédisposant à l’émergence d’une synchronisation neuronale. Nous explorons ensuite les processus cérébraux mis en jeu lors de la pratique de la musique au travers d’études réalisées en neurosciences. Par la suite nous présentons plusieurs méthodes permettant de calculer des indices de couplage cérébral sur les données récoltées lors d’expériences en hyperscanning. Nous y décrivons en particulier les méthodes de séparation de source conjointe (jBSS) dont l’avantage est de se rapprocher d’une réalité anatomique et physiologique, ainsi que de prendre en compte l’information inter-sujets lors de l’estimation des sources. Enfin, nous détaillons notre contribution au champ des neurosciences sociales sous la forme d’une expérience longitudinale en hyperscanning-EEG. Elle étudie l’interaction sociale de pianistes à quatre mains lors de l’apprentissage d’un morceau de musique sur une période de deux mois. Nous mettons en évidence qu’il existe une corrélation entre l’augmentation de la performance musicale au cours du temps, la synchronisation cérébrale et la qualité de la relation entre les musiciens
The aim of our research is to understand the neural bases of social interaction in a musical performance context with tools from neuroscience (electroencephalography: EEG) and signal processing. This manuscript first presents a state of the art of recent studies in the field of hyperscanning. We introduce our recommendations on the prerequisites and methodology to design experiments facilitating the emergence of neuronal synchronization. We then explore the cerebral processes involved in the practice of music through studies in neuroscience of music. Subsequently we present several methods to calculate brain coupling on data collected during experiments in hyperscanning. We describe in particular the methods of joint blind source separation (jBSS) whose advantages are to approach anatomical and physiological reality, as well as taking into account inter-subject information when estimating sources. Finally, we detail our contribution to the field of social neuroscience with a longitudinal experience in hyperscanning-EEG. We studied social interaction from musicians playing four hands piano over a two-month period. We highlight a correlation between increased musical performance over time, cerebral synchronization and quality of the relationship between the pianists

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.

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.

abstract: The use of electromyography (EMG) signals to characterize muscle fatigue has been widely accepted. Initial work on characterizing muscle fatigue during isometric contractions demonstrated that its frequency decreases while its amplitude increases with the onset of fatigue. More recent work concentrated on developing techniques to characterize dynamic contractions for use in clinical and training applications. Studies demonstrated that as fatigue progresses, the EMG signal undergoes a shift in frequency, and different physiological mechanisms on the possible cause of the shift were considered. Time-frequency processing, using the Wigner distribution or spectrogram, is one of the techniques used to estimate the instantaneous mean frequency and instantaneous median frequency of the EMG signal using a variety of techniques. However, these time-frequency methods suffer either from cross-term interference when processing signals with multiple components or time-frequency resolution due to the use of windowing. This study proposes the use of the matching pursuit decomposition (MPD) with a Gaussian dictionary to process EMG signals produced during both isometric and dynamic contractions. In particular, the MPD obtains unique time-frequency features that represent the EMG signal time-frequency dependence without suffering from cross-terms or loss in time-frequency resolution. As the MPD does not depend on an analysis window like the spectrogram, it is more robust in applying the timefrequency features to identify the spectral time-variation of the EGM signal.
Dissertation/Thesis
M.S. Electrical Engineering 2012

Introduction Electromyography (EMG) is a technique used to study the activity of muscle through detection and analysis of the electrical signals generated during muscular contractions. Electromyographic activity is recorded from skeletal muscles to obtain information about their anatomy and physiology. Electromyography, in interplay with various anatomical techniques, provides the present knowledge of the structural organization and the nervous control of muscle. EMG is the prime source of information about the status of the neuromuscular system, and EMG has developed into a diagnostic tool that allows the clinician to follow changes in nerve and muscle caused by neuromuscular diseases. EMG provides both invasive and noninvasive means for the study of muscular functions [1, 2]. It is also useful in interpreting pathologic states of musculoskeletal or neuromuscular systems [3, 4]. In particular, EMG offers valuable information concerning the timing of muscular activity and its relative intensity [5, 6]. Standard EMG is typically recorded from fine wire or two surface electrodes placed at discrete sites over a muscle or muscle belly. Currently surface grid electrode EMG is widely used. The cell bodies of these neurons reside in the brainstem and spinal cord. The interfacing fiber between motor neuron and muscle is called axon. At the distal end, an axon divides 1 into many terminal branches. Each terminal branch innervates a group of muscle fibers. When a nerve signal approaches the end of an axon, it spreads out over all its terminal branches and stimulates all the muscle fibers supplied by them. So, all the excited muscle fibers contract almost simultaneously. Since they behave as a single functional unit, one nerve fiber and all the muscle fibers innervated by it are called a motor unit (MU) [7, 8]. Generally, the muscle fibers of a motor unit are distributed throughout muscle rather than being clustered together. The fine control of the muscle force is performed through the intricate mechanism and interaction of the brain and muscle. During contraction, these motor units are recruited systematically and the recruited motor units discharge in a train of pulses in a complex manner [9, 10]. The recorded EMG is the temporal summation of all the recruited motor unit action potential trains. Because movement is controlled by motor unit activity, an understanding of motor unit physiology can have a significant impact on the evaluation and treatment of movement disorders. The neuromuscular system is an intricate physiological organization of brain, nerve and muscle. These neural control properties are not well understood mostly because of the experimental difficulties in quantifying the neural input to the muscle. Moreover, the muscle itself is a complex system. It is necessary to address these complexities as accurately as possible. Understanding of these complex systems facilitates the understanding of EMG generation, which is a highly complex signal by itself.

A finite element (FE) model for the generation of single fiber action potentials (SFAPs) in a muscle undergoing various degrees of fiber shortening has been developed. The muscle is assumed to be fusiform with muscle fibers following a curvilinear path described by a Gaussian function. Different degrees of fiber shortening are simulated by changing the parameters of the fiber path and maintaining the volume of the muscle constant. The conductivity tensor is adapted to the muscle fiber orientation. At each point of the volume conductor, the conductivity of the muscle tissue in the direction of the fiber is larger than that in the transversal direction. Thus, the conductivity tensor changes point-by-point with fiber shortening, adapting to the fiber paths. An analytical derivation of the conductivity tensor is provided. The volume conductor is then studied with an FE approach using the analytically derived conductivity tensor (Mesin, Joubert, Hanekom, Merletti&Farina 2006). Representative simulations of SFAPs with the muscle at different degrees of shortening are presented. It is shown that the geometrical changes in the muscle, which imply changes in the conductivity tensor, determine important variations in action potential shape, thus affecting its amplitude and frequency content. The model is expanded to include the simulation of motor unit action potentials (MUAPs). Expanding the model was done by assigning each single fiber (SF) in the motor unit (MU) a random starting position chosen from a normal distribution. For the model 300 SFs are included in an MU, with an innervation zone spread of 12 mm. Only spatial distribution was implemented. Conduction velocity (CV) was the same for all fibers of the MU. Representative simulations for the MUAPs with the muscle at different degrees of shortening are presented. The influence of interelectrode distance and angular displacement are also investigated as well as the influence of the inclusion of the conductivity tensor. It has been found that the interpretation of surface electromyography during movement or joint angle change is complicated owing to geometrical artefacts i.e. the shift of the electrodes relative to the muscle fibers and also because of the changes in the conductive properties of the tissue separating the electrode from the muscle fibers. Detection systems and electrode placement should be chosen with care. The model provides a new tool for interpreting surface electromyography (sEMG) signal features with changes in muscle geometry, as happens during dynamic contractions.
Dissertation (MEng)--University of Pretoria, 2008.
Electrical, Electronic and Computer Engineering
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