Determining tendon tension in the finger is essential to understanding forces that may be detrimental to hand function. Direct measurement is not feasible, making biomechanical modelling the best way to estimate these forces. In this study, the intrinsic muscles and extensor mechanism were added to an existing model of the index finger, and as such, it has been named the Intrinsic model. The Intrinsic model of the index finger has 4 degrees of freedom and 7 muscles (with 14 components). Muscle properties and paths for all extrinsic and intrinsic muscles were derived from the literature. Two models were evaluated, the Intrinsic model and the model it was adapted from (identified in this thesis as the Extrinsic-only model). To complement the model, multiple static optimization solution methods were also developed that allowed for EMG-constrained solutions and applied objective functions to promote co-contraction. To test the models and solution methods, 10 participants performed 9 static pressing tasks at 3 force levels, and 5 free motion dynamic tasks at 2 speeds. Kinematics, contact forces, and EMG (from the extrinsic muscles and first dorsal interosseous) were collected. For all solution methods, muscle activity predicted using the Intrinsic model was compared to activity from the model currently available through open-source software (OpenSim). Just by using the Intrinsic model, co-contraction increased by 16% during static palmar pressing tasks. The EMG-constrained solution methods gave a smaller difference between predicted and experimental activity compared to the optimization-only approach (p < 0.03). The model and solution methods developed in this thesis improve co-contraction and tendon tension estimates in the finger. As such, this work contributes to our understanding of the control of the hand and the forces that may be detrimental to hand function. / Thesis / Master of Science (MSc)

Les signaux électromyographiques de surface (sEMG) correspondent aux signaux électriques composés par les potentiels d’action produits par les unités motrices d’un muscle actif et enregistrés par des électrodes de surface. Les signaux sEMG sont largement utilisés dans la médicine, le contrôle des prothèses et plus généralement dans les études biomécaniques portant sur l’analyse du mouvement humain. Les signaux sEMG sont très souvent utilisés comme un indicateur d’activation musculaire.Bien que présentant un intérêt évident, l’utilisation de ces signaux reste difficile compte tenu qu’ils sont souvent susceptibles d’interférence (diaphonie, ou plus communément « crosstalk ») entre les muscles contigus, parfois même éloignés. Cette contamination croisée est particulièrement présente pour des muscles présents dans un volume restreint, ce qui est le cas des muscles extenseur de l’index et du petit doigt, extensor indicis et extensor digiti minimi. L’interférence induit la réduction de la précision de l’estimation des activations musculaires et reste, à ce titre, un problème important et récurrent de la biomécanique. Afin que les signaux sEMG puissent être utilisés de manière plus robuste en biomécanique, il convient de réduire cette interférence avant de procéder à l’estimation des activations musculaires. Les activations individuelles des muscles participant au mouvement correctement estimées peuvent être utilisées comme données d’entrées d’un modèle biomécanique. Cette démarche, nommée dynamique directe, permet notamment d’estimer la force externe produite par le système et dans un second temps de comparer cette dernière avec la mesure réalisée grâce à un système dynamométrique. En ce sens cette démarche permet une validation indirecte des estimations réalisées à partir des signaux sEMG. Dans le cadre de cette thèse, nous avons modélisé le doigt et plus particulièrement le mécanisme extenseur qui est une structure qui transmet les forces des muscles-extenseurs aux articulations digitales. Cette structure est très mal connue du point de vue biomécanique et le plus souvent représentée par un ensemble des coefficients établis sur l’analyse de mains de cadavres dans des situations très particulières et standardisées (doigts en extension). Ainsi, l’objectif de ce travail de thèse était double : (1) améliorer l’estimation de la force au bout du doigt à partir des mélanges des sEMG sur la base d’extraction des activations des signaux sEMG des muscles extensor indicis et extensor digiti minimi, et (2) modélisation biomécanique du mécanisme extenseur du doigt. Pour cela, les signaux sEMG ont été enregistrés avec une matrice d’électrodes de surface haute densité à 64 capteurs. Ensuite, l’extraction des activations musculaires a été réalisée sur la base d’une procédure de classification des potentiels détectés en utilisant les invariants musculaires que sont la direction de propagation et la profondeur de l’unité motrice à l’origine du signal.Dans un deuxième temps, un modèle biomécanique précis du mécanisme extenseur du doigt a été créé, qui contient les tendons et les principaux ligaments représentés par des bandes et des surfaces élastiques. Un algorithme de paramétrage du modèle a été proposé. Ce type d ‘approche est nécessaire pour mieux décrire les déformations du système anatomique dans des situations de mouvement sain ou pathologique.Cette démarche a montré qu’elle était pertinente pour l’étude biomécanique du doigt. Elle présente des utilisations judicieuses pour les études biomécaniques portant sur l’évaluation clinique, la réhabilitation et le contrôle des prothèses myoélectriques. / The surface electromyographic signals (SEMG) are the electric signals, composed of electric potentials. These potentials are produced by the recruited motor units of an active muscle and captured by the surface electrodes. The SEMG signals are widely used in medicine, prosthesis control and biomechanical studies as an indicator of muscle activity.However, SEMG measurements are usually subjects of crosstalk or interference from nearby muscles. It appears when two or more muscles situated close to each other are active during a SEMG recording. An example of such muscles are the extensors of index and little finger, extensor indicis and extensor digiti minimi, situated close to each other and creating a significant amount of mutual crosstalk when simultaneously active. The crosstalk causes precision decrease of SEMG-based estimation of muscle activations. Hence, the crosstalk-reducing problem must be preliminary solved before muscle activation evaluation.Once the activations of individual muscles are estimated from the mixture, they may be used as an input of a finger biomechanical model to calculate a fingertip force. These models usually contain an extensor mechanism of the finger, which is a structure, transmitting the force from the extensor muscles to the finger joints. This structure is often taken into account as a set of coefficients. However, there is a lack of study about how these coefficients vary with posture, applied force, and subject variability.The purpose of this work is to improve the finger force estimation from the crosstalk-contaminated signals for isometric tasks by extracting the activations of individual muscles and improving the finger biomechanical model.Firstly, the SEMG signals were recorded with high-density surface electromyographic (HD-EMG) electrode matrix. The extraction was based on classifying the detected potentials according their propagation direction and depth of originating motor unit.Secondly, a precise biomechanical model of the finger extensor mechanism was created, containing the principal tendons and ligaments. The algorithm of the model parametrization was proposed as well.The proposed methods of muscle activation estimation along with the created extensor mechanism model may be used for calculating the fingertip force and internal tissues deformations for normal or pathological fingers.

The knee joint plays an essential role in the human musculoskeletal system. It has evolved to withstand extreme loading conditions, while providing almost frictionless joint movement. However, its performance may be disrupted by disease, anatomical deformities, soft tissue imbalance or injury. Knee disorders are often puzzling, and accurate diagnosis may be challenging. Current evaluation approach is usually limited to a detailed interview with the patient, careful physical examination and radiographic imaging. The X-ray screening may reveal bone degeneration, but does not carry sufficient information of the soft tissue conditions. More advanced imaging tools such as MRI or CT are available, but expensive, time consuming and can be used only under static conditions. Moreover, due to limited resolution the radiographic techniques cannot reveal early stage arthritis. The arthroscopy is often the only reliable option, however due to its semi-invasive nature, it cannot be considered as a practical diagnostic tool. Therefore, the motivation for this work was to combine three scientific methods to provide a comprehensive, non-invasive evaluation tool bringing insight into the in vivo, dynamic conditions of the knee joint and articular cartilage degeneration. Electromyography and inverse dynamics were employed to independently determine the forces present in several muscles spanning the knee joint. Though both methods have certain limitations, the current work demonstrates how the use of these two methods concurrently enhances the biomechanical analysis of the knee joint conditions, especially the performance of the extensor mechanism. The kinetic analysis was performed for 12 TKA, 4 healthy individuals in advanced age and 4 young subjects. Several differences in the knee biomechanics were found between the three groups, identifying age-related and post-operative decrease in the extensor mechanism efficiency, explaining the increased effort of performing everyday activities experienced by the elderly and TKA subjects. The concept of using accelerometers to assess the cartilage degeneration has been proven based on a group of 23 subjects with non-symptomatic knees and 52 patients suffering from knee arthritis. Very high success (96.2%) of pattern classification obtained in this work clearly demonstrates that vibroarthrography is a promising, non-invasive and low-cost technique offering screening capabilities.