<p>Biomechanical and neural factors have both been suggested to contribute to the limited independence of finger movement and involuntary force production. The purpose of this study was to evaluate the degree of finger independence by examining the activity of the four compartments of extensor digitorum (ED) and flexor digitorum superficialis (FDS) using surface electromyography and involuntary force production in the non-task fingers using methods such as the “enslaving effect” (EE) and the “selectivity index” (SI). Twelve male participants performed a series of 5-second sub-maximal exertions at 5, 25, 50 and 75% of maximum using isometric isotonic and ramp finger flexion and extension exertions. Ramp exertions were performed from 0 to 85% of each finger’s maximum force with ascending and descending phases taking 4.5 seconds each with 0.5 seconds of plateau at 85%. Lower EE and higher SI (more selective force production) was found in flexion exertions compared to extension partially due to the higher activity of the antagonist ED compartments counterbalancing the involuntary activation of the non-task FDS compartments. Minimal FDS activity was seen during extension exertions. At forces up to and including 50%, both EE and muscle activity of the non-task compartments were significantly higher in descending exertions than the isotonic or ascending exertions. The selectivity index was also lower during the descending flexion and extension exertions at 25 and 50% MVC exertions. Up to mid-level forces, both finger proximity and contraction mode affects involuntary force production and muscle activation while at higher forces only finger proximity (and not the exertion mode) contributes to finger independence. The fingers were less selective at higher exertion levels (75% MVC) and all 3 exertion modes resulted in similar SI at 75% MVC in all flexion and extension exertions.</p> / Master of Science in Kinesiology

Les prothèses myoélectriques modernes peuvent être dotées de plusieurs degrés de liberté ce qui nécessite plusieurs signaux musculaires pour en exploiter pleinement les capacités. Pour obtenir plus de signaux, il nous a semblé prometteur d'expérimenter si les 6 compartiments du biceps brachial pouvaient être mis sous tension de façon volontaire et obtenir ainsi 6 signaux de contrôle au lieu d'un seul comme actuellement. Des expériences ont donc été réalisées avec 10 sujets normaux. Des matrices d'électrodes ont été placées en surface au-dessus du chef court et long du biceps pour recueillir les signaux électromyographiques (EMG) générés par le muscle lors de contractions effectuées alors que les sujets étaient soit assis, le coude droit fléchi ~ 100 ° ou debout avec le bras droit tendu à l'horizontale dans le plan coronal (sur le côté). Dans ces deux positions, la main était soit en supination, soit en position neutre, soit en pronation. L'amplitude des signaux captés au-dessus du chef court du muscle a été comparée à ceux obtenus à partir du chef long. Pour visualiser la forme du biceps sous les électrodes l'imagerie ultrasonore a été utilisée. En fonction de la tâche à accomplir, l'activité EMG a était plus importante soit dans un chef ou dans l'autre. Le fait de pouvoir activer préférentiellement l'un des 2 chefs du biceps, même si ce n'est pas encore de façon complètement indépendante, suggère que l'utilisation sélective des compartiments pourrait être une avenue possible pour faciliter le contrôle des prothèses myoélectriques du membre supérieur. / The latest myoelectric prostheses have several degrees of freedom and therefore require a large number of myoelectric signals to fully exploit their capabilities. Muscle compartments, which are intra-muscular subdivisions innervated by an individual muscle nerve branch, can be exploited to provide additional independent muscle control sites to operate such prostheses. This research presents a work to investigate the activation of the 6 biceps brachii compartments in healthy subjects to see if they have the ability to activate those compartments voluntarily. Therefore, electromyographic (EMG) signals were recorded from an array of seven and ten pairs of equally spaced surface electrodes positioned across the short and long head of the biceps of ten healthy subjects. The EMG signals are collected in two positions: 1) with the subject seated, right elbow flexed ~100°, and 2) with the subject standing with the right arm extended horizontally in the coronal plane (90°shoulder abduction). In both positions, the hand is either fully supinated, neutral, or fully pronated. The average root mean square value of the EMG signals obtained from the pairs of electrodes positioned over the short head are compared with the average obtained for the other pairs placed over the biceps long head. Ultrasound imaging also used to visualize the long and short heads of the biceps in flexed and extended arm while the hand was in different postures. Depending on the task to be accomplished, activity was larger in one head or in the other. Being able to activate either head of the biceps, while not yet completely independently, suggests that the selective use of compartments could be a possible avenue for controlling upper limb myoelectric prostheses.

Title: Electromyographic Analysis of the Different Handlebars Grips Effect on the Shoulder Blade Fixators Aims: The main objective of this thesis is to carry out a methodological study on the impact of different handlebars grips on the shoulder blades fixators. Result of the grip change is not only reduction of the upper fixators activity and the increase in the lower fixators activity but also co-contraction index increase and muscle fatigue reduction. Based on the hypothesis we assumed that the highest activity rate of the lower blades fixators and the lowest activity rate of the upper blades fixators will be during the handlebars brakes grip. The highest co- contraction index will occur during the handlebar brakes grip and the maximum muscular fatigue will occur during the grip at the top of the handlebars. The first part of the thesis is focused on the literature search and facts retrieval pertaining to the studied issue. In the second special part of the thesis we conducted a research during which the muscle activity of the upper and lower blade fixators was recorded. Three different handlebars grips were considered during the measurement using the cycle ergometer. Methods: Muscle activity was scanned and recorded using surface EMG. Maximal voluntary contraction of selected muscles based on...

Les prothèses myoélectriques modernes peuvent être dotées de plusieurs degrés de liberté ce qui nécessite plusieurs signaux musculaires pour en exploiter pleinement les capacités. Pour obtenir plus de signaux, il nous a semblé prometteur d'expérimenter si les 6 compartiments du biceps brachial pouvaient être mis sous tension de façon volontaire et obtenir ainsi 6 signaux de contrôle au lieu d'un seul comme actuellement. Des expériences ont donc été réalisées avec 10 sujets normaux. Des matrices d'électrodes ont été placées en surface au-dessus du chef court et long du biceps pour recueillir les signaux électromyographiques (EMG) générés par le muscle lors de contractions effectuées alors que les sujets étaient soit assis, le coude droit fléchi ~ 100 ° ou debout avec le bras droit tendu à l'horizontale dans le plan coronal (sur le côté). Dans ces deux positions, la main était soit en supination, soit en position neutre, soit en pronation. L'amplitude des signaux captés au-dessus du chef court du muscle a été comparée à ceux obtenus à partir du chef long. Pour visualiser la forme du biceps sous les électrodes l'imagerie ultrasonore a été utilisée. En fonction de la tâche à accomplir, l'activité EMG a était plus importante soit dans un chef ou dans l'autre. Le fait de pouvoir activer préférentiellement l'un des 2 chefs du biceps, même si ce n'est pas encore de façon complètement indépendante, suggère que l'utilisation sélective des compartiments pourrait être une avenue possible pour faciliter le contrôle des prothèses myoélectriques du membre supérieur. / The latest myoelectric prostheses have several degrees of freedom and therefore require a large number of myoelectric signals to fully exploit their capabilities. Muscle compartments, which are intra-muscular subdivisions innervated by an individual muscle nerve branch, can be exploited to provide additional independent muscle control sites to operate such prostheses. This research presents a work to investigate the activation of the 6 biceps brachii compartments in healthy subjects to see if they have the ability to activate those compartments voluntarily. Therefore, electromyographic (EMG) signals were recorded from an array of seven and ten pairs of equally spaced surface electrodes positioned across the short and long head of the biceps of ten healthy subjects. The EMG signals are collected in two positions: 1) with the subject seated, right elbow flexed ~100°, and 2) with the subject standing with the right arm extended horizontally in the coronal plane (90°shoulder abduction). In both positions, the hand is either fully supinated, neutral, or fully pronated. The average root mean square value of the EMG signals obtained from the pairs of electrodes positioned over the short head are compared with the average obtained for the other pairs placed over the biceps long head. Ultrasound imaging also used to visualize the long and short heads of the biceps in flexed and extended arm while the hand was in different postures. Depending on the task to be accomplished, activity was larger in one head or in the other. Being able to activate either head of the biceps, while not yet completely independently, suggests that the selective use of compartments could be a possible avenue for controlling upper limb myoelectric prostheses.

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.

Informações sobre as características funcionais e estruturais da unidade motora (UM) são altamente relevantes em investigações fisiológicas e nos estudos clínicos das disfunções neuromusculares. A eletromiografia (EMG) é um método adequado para obtenção dessas informações. Entretanto, devido à dificuldade na separação da atividade individual de uma unidade motora das outras que estão simultaneamente ativas, seu uso em clínica prática se dá comumente através de métodos invasivos, empregando eletrodos de agulha ou fios implantados. Apesar da EMG de superfície ser não-invasiva e, portanto mais apropriada para aplicações clínicas, não é usada em clínica porque não há até o presente um método satisfatório para decomposição do sinal EMG de superfície. Um EMG de superfície é muito mais difícil de decompor devido a significante superposição dos Potenciais de Ação das UMs (MUAPs) e a relação sinal-ruído relativamente baixa, se comparada aos métodos invasivos. Defendemos que a separação da atividade individual das UMs pode ser feita de modo não-invasivo aliando-se técnicas de aquisição altamente especializadas com técnicas usadas em reconhecimento de padrões. Desenvolvemos um método para decomposição de EMGs de superfície, a partir do qual foi possível extrair características relevantes das UMs, que permitem seu uso em avaliação e diagnóstico de desordens neuromusculares. Em nossa abordagem, o sinal EMG é inicialmente captado sob contração isométrica fraca usando eletrodos desuperfície. O sinal EMG bruto passa em seguida por um filtro Diferencial Passa-Baixas Ponderado (DPBP) em série com um detector de picos, que detecta os picos de MUAPs e extrai suas formas de onda. Na sequência, o conjunto de MUAPs extraído é classificado por uma rede neural SOM, e os MUAPs agrupados pela similaridade de suas formas de onda. No próximo passo a informação temporal dos disparos é checada, eliminando possíveis erros de classificação, e finalmente os Trens de MUAPs (MUAPTs) das UMs individuais são reconstituídos do EMG original. As estatísticas de disparos (IPI) bem como as formas de ondas dos MUAPs das respectivas UMs são então extraídas e armazenadas para estudos posteriores. Resultados preliminares obtidos com EMGs normais e patológicos, extraídos de membros superiores sob contração fraca, indicam que, o método mostrou-se apto a decompor EMGs de superfícies, além de potencial para aplicações em estudos clínicos não-invasivos de disfunções neuromusculares.Informações sobre as características funcionais e estruturais da unidade motora (UM) são altamente relevantes em investigações fisiológicas e nos estudos clínicos das disfunções neuromusculares. A eletromiografia (EMG) é um método adequado para obtenção dessas informações. Entretanto, devido à dificuldade na separação da atividade individual de uma unidade motora das outras que estão simultaneamente ativas, seu uso em clínica prática se dá comumente através de métodos invasivos, empregando eletrodos de agulha ou fios implantados. Apesar da EMG de superfície ser não-invasiva e, portanto mais apropriada para aplicações clínicas, não é usada em clínica porque não há até o presente um método satisfatório para decomposição do sinal EMG de superfície. Um EMG de superfície é muito mais difícil de decompor devido a significante superposição dos Potenciais de Ação das UMs (MUAPs) e a relação sinal-ruído relativamente baixa, se comparada aos métodos invasivos. Defendemos que a separação da atividade individual das UMs pode ser feita de modo não-invasivo aliando-se técnicas de aquisição altamente especializadas com técnicas usadas em reconhecimento de padrões. Desenvolvemos um método para decomposição de EMGs de superfície, a partir do qual foi possível extrair características relevantes das UMs, que permitem seu uso em avaliação e diagnóstico de desordens neuromusculares. Em nossa abordagem, o sinal EMG é inicialmente captado sob contração isométrica fraca usando eletrodos desuperfície. O sinal EMG bruto passa em seguida por um filtro Diferencial Passa-Baixas Ponderado (DPBP) em série com um detector de picos, que detecta os picos de MUAPs e extrai suas formas de onda. Na sequência, o conjunto de MUAPs extraído é classificado por uma rede neural SOM, e os MUAPs agrupados pela similaridade de suas formas de onda. No próximo passo a informação temporal dos disparos é checada, eliminando possíveis erros de classificação, e finalmente os Trens de MUAPs (MUAPTs) das UMs individuais são reconstituídos do EMG original. As estatísticas de disparos (IPI) bem como as formas de ondas dos MUAPs das respectivas UMs são então extraídas e armazenadas para estudos posteriores. Resultados preliminares obtidos com EMGs normais e patológicos, extraídos de membros superiores sob contração fraca, indicam que, o método mostrou-se apto a decompor EMGs de superfícies, além de potencial para aplicações em estudos clínicos não-invasivos de disfunções neuromusculares. / Information on the functional and structural characteristics of the motor unit (MU) they are highly important in physiologic investigations and in the clinical studies of the neuromuscular dysfunctions. The electromyography (EMG) it is an appropriate method for obtaining of that information. However, due to the difficulty in the separation of the individual activity of a motor unit of the another that are simultaneously active, your use in practical clinic happen commonly through methods invasive, employing needle electrodes or implanted threads. In spite of surface EMG to be non-invasive and, therefore more appropriate for clinical applications, it is not used at clinic because there is not until the present a satisfactory method for decomposition of the surface EMG sign. A surface EMG is much more difficult of decomposing due to significant overlap of the Motor Unit Action Potentials (MUAPs) and the relationship sign-noise relatively low, if compared to the invasive methods. We defended that the separation of the individual activity of MUs can be made in way non-invasive allying highly specialized acquisition techniques with techniques used in recognition of patterns. We developed a method for decomposition of surface EMGs, starting from which was possible to extract important characteristics of MUs, which allow your use in evaluation and diagnosis of neuromuscular disorders. In our approach, the sign EMG is captured initially under weak isometriccontraction using surface electrodes. The sign EMG raw raisin soon after for a Biased Low-Pass Differential filter (BLPD) in series with a detector of peaks, that detects the peaks of MUAPs and it extracts your wave forms. In the sequence, a SOM neural network classifies the set of extracted MUAPs, and MUAPs are clustered by the similarity in your wave shape. In the next step the temporal information of the discharges is checked, eliminating possible classification mistakes, and finally the MUAPs Trains (MUAPTs) of individual MUs they are reconstituted of original EMG. The statistics of discharges (IPI) as well as the forms of waves of MUAPs of respective MUs are then extracted and stored for subsequent studies. Results preliminaries obtained with normal and pathological EMGs, extracted of superior members under weak contraction, they indicate that, the method was shown capable to decompose surfaces EMGs, besides potential for applications in clinical studies non-invasive of neuromuscular dysfunctions.

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.