This thesis reports research on analysis of the variance of surface electromyogram (sEMG) for healthy participants and people suffering with Lower Back Pain (LBP) when they are walking and running. SEMG signal recorded when the participants were walking and running on a treadmill. The strength and duration of the muscle activity for each heel strike were the features. The results indicate that there was no significant difference in the variance and in the change of variance over time of the amplitude between the two groups when the participants were walking. However when the participants were running, there was a significant difference in the two cohorts. While there was an increase in the total variance over the duration of the exercise for both the groups, the increase in variance of the LBP group was much greater (order of ten times) compared with the participants with healthy backs. The difference between the two groups was also very significant when observing the change of variance over the duration of the exercise. From these results, it is suggested that variance of sEMG of the muscles of the lower back, recorded when the participants are running, can be used to identify LBP patients.

Identifying finger and wrist flexion based actions using single channel surface electromyogram have a number of rehabilitation, defence and human computer interface applications. These applications are currently infeasible because of unreliability in classification of sEMG when the level of muscle contraction is low and when there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during maintained wrist and finger flexion. It has been established in literature that surface electromyogram (sEMG) and other such biosignals are fractal signals. Some researchers have determined that fractal dimension (FD) is related to strength of muscle contraction. On careful analysis of fractal properties of sEMG, this research work has established that FD is related to the muscle size and complexity and not to the strength of muscle contraction. The work has also identified a novel feature, maximum fractal length (MFL) of the signal, as a good measure of strength of contraction of the muscle. From the analysis, it is observed that while at high level of contraction, root mean square (RMS) is an indicator of strength of contraction of the muscle, this relationship is not very strong when the muscle contraction is less than 50% maximum voluntary contraction. This work has established that MFL is a more reliable measure of strength of contraction compared to RMS, especially at low levels of contraction. This research work reports the use of fractal properties of sEMG to identify the small changes in strength of muscle contraction and the location of the active muscles. It is observed that fractal dimension (FD) of the signal is related with the properties of the muscle while maximum fractal length (MFL) is related to the strength of contraction of the associated muscle. The results show that classifying MFL and FD of a single channel sEMG from the forearm it is possible to accurately identify a set of finger and wrist flexion based actions even when the muscle activity is very weak. It is proposed that such a system could be used to control a prosthetic hand or for human computer interface.

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.