We describe a semi-automatic technique for modeling and animating complex musculoskeletal systems using a strand based muscle model. Using our interactive tools, we are able to generate the motion of tendons and muscles under the skin of a traditionally animated character. This is achieved by integrating the traditional animation pipeline with a biomechanical simulator capable of dynamic simulation with complex routing constraints on muscles and tendons. We integrate our musculoskeletal modeling and animation toolkit into a professional 3D production environment, thereby enabling artists and scientists to create complex musculoskeletal systems that were previously inaccessible to them. We demonstrate the applications of our tools to the visual effects industry with several animations of the human hand and applications to the biomechanics community with a novel model of the human shoulder.

We describe a semi-automatic technique for modeling and animating complex musculoskeletal systems using a strand based muscle model. Using our interactive tools, we are able to generate the motion of tendons and muscles under the skin of a traditionally animated character. This is achieved by integrating the traditional animation pipeline with a biomechanical simulator capable of dynamic simulation with complex routing constraints on muscles and tendons. We integrate our musculoskeletal modeling and animation toolkit into a professional 3D production environment, thereby enabling artists and scientists to create complex musculoskeletal systems that were previously inaccessible to them. We demonstrate the applications of our tools to the visual effects industry with several animations of the human hand and applications to the biomechanics community with a novel model of the human shoulder.

We describe a semi-automatic technique for modeling and animating complex musculoskeletal systems using a strand based muscle model. Using our interactive tools, we are able to generate the motion of tendons and muscles under the skin of a traditionally animated character. This is achieved by integrating the traditional animation pipeline with a biomechanical simulator capable of dynamic simulation with complex routing constraints on muscles and tendons. We integrate our musculoskeletal modeling and animation toolkit into a professional 3D production environment, thereby enabling artists and scientists to create complex musculoskeletal systems that were previously inaccessible to them. We demonstrate the applications of our tools to the visual effects industry with several animations of the human hand and applications to the biomechanics community with a novel model of the human shoulder. / Science, Faculty of / Computer Science, Department of / Graduate

In the classic Hill model for muscle contraction, the split between the muscle and tendon is arbitrary and the problem lacks a unique solution. Instead of reformulating the problem to a differential-algebraic equation and solving for a set of initial conditions, a constant tendon length is commonly assumed in musculoskeletal simulation tools. This assumption has not been thoroughly tested and introduces errors of unknown magnitude to the simulations. In this thesis, the contractile element of the Hill model is modelled as a friction clutch in parallel to a viscous damper. This provides an evolution law for the muscle length by which the muscle speed is numerically calculated taking into account a non-zero tendon speed. A simple biceps curl is simulated with the friction clutch model and compared to corresponding commercial musculoskeletal simulations. Overall, the results are similar, in particular for the muscle lengths which are almost identical in every simulation (0.00-0.42% difference). The difference in tendon speed is 0.00-3.26%, with upwards tendencies. In general, the error percentage of the tendon speed appears to decrease by the same amount that the contraction speed is reduced. Conclusively, it can be said that the introduced friction clutch model delivers comparative outcomes to a commercial musculoskeletal simulation software, while not assuming a constant tendon length. However, while presenting a relatively simple solution, an increased computation time is to be expected due to the need of a differential equation solver. Further investigation regarding implementation and computing times in more complex simulations may provide an alternative approach to conventional musculoskeletal simulations.

Tracking human hand motions has raised more attention due to the recent advancements of virtual reality (Rheingold, 1991) and prosthesis control (Antfolk et al., 2010). Surface electromyography (sEMG) has been the predominant method for sensing electrical activity in biomechanical studies, and has also been applied to motion tracking in recent years. While most studies focus on the classification of human hand motions within a predefined motion set, the prediction of continuous finger joint angles and wrist angles remains a challenging endeavor. In this research, a biomechanical knowledge-driven data fusion strategy is proposed to predict finger joint angles and wrist angles. This strategy combines time series data of sEMG signals and simulated muscle features, which can be extracted from a biomechanical model available in OpenSim (Delp et al., 2007). A support vector regression (SVR) model is used to firstly predict muscle features from sEMG signals and then to predict joint angles from the estimated muscle features. A set of motion data containing 10 types of motions from 12 participants was collected from an institutional review board approved experiment. A hypothesis was tested to validate whether adding the simulated muscle features would significantly improve the prediction performance. The study indicates that the biomechanical knowledge-driven data fusion strategy will improve the prediction of new types of human hand motions. The results indicate that the proposed strategy significantly outperforms the benchmark date-driven model especially when the users were performing unknown types of motions from the model training stage. The proposed model provides a possible approach to integrate the simulation models and data fusion models in human factors and ergonomics. / Master of Science / Hand motion tracking is a promising technique for the development of virtual reality and prosthesis. Identifying hand motions based on sensor data is the fundamental step to realize motion tracking. Among all the tracking techniques, surface electromyography (sEMG) is a type of electrical signals that has been proven useful in predicting hand motions in recent years, since sEMG signals can directly reflect muscle activities, and hand motions are controlled by muscle groups. While most studies focus on the classification of human hand motions within a predefined motion set, the prediction of continuous finger joint angles and wrist angles remains a challenging endeavor. In this research, a biomechanical knowledge-driven data fusion strategy was proposed to predict finger joint angles and wrist angles. More specifically, this strategy combined a statistical model with a biomechanical simulation model, and a hypothesis was tested to validate whether adding the biomechanical simulation model would significantly improve the prediction performance. A set of sEMG signals containing 10 types of motions from 12 participants was collected from an institutional review board approved experiment, in order to test the proposed strategy. The results indicate that the proposed strategy significantly outperforms the benchmark statistical models especially when users were performing unknown types of motions from the model training stage. The proposed strategy provides a possible approach to integrate the simulation models and data-driven models in human factors and ergonomics.

La réalité augmentée permet d'aider les chirurgiens à localiser pendant l'opération la position des structures d'intérêt, comme les vaisseaux sanguins. Dans le cadre de la chirurgie laparoscopique, les modèles 3D affichés durant l'intervention ne correspondent pas à la réalité à cause des déformations dues au pneumopéritoine. Cette thèse a pour objectif de corriger ces déformations afin de fournir un modèle du foie réaliste. Nous proposons de déformer le modèle préopératoire du foie à partir d'une acquisition intraopératoire de la surface antérieure du foie. Un champ de déformations entre les modèles préopératoire et intraopératoire est calculé à partir de la distance géodésique à des repères anatomiques. De plus, une simulation biomécanique du pneumopéritoine est réalisée pour prédire la position de la cavité abdomino-thoracique qui est utilisée comme condition limite. L'évaluation de cette méthode montre que l'erreur de position du foie et de ses structures internes est réduite à 1cm. / Augmented reality can provide to surgeons during intervention the positions of critical structures like vessels. The 3D models displayed during a laparoscopic surgery intervention do not fit to reality due to pneumperitoneum deformations. This thesis aim is to correct these deformations to provide a realistic liver model during intervention. We propose to deform the preoperative liver model according to an intraoperative acquisition of the liver anterior surface. A deformation field between the preoperative and intraoperative models is computed according to the geodesic distance to anatomical landmarks. Moreover, a biomechanical simulation is realised to predict the position of the abdomino-thoracic cavity which is used as boundary conditions. This method evaluation shows that the position error of the liver and its vessels is reduced to 1cm.

En chirurgie mini-invasive guidée par l’image, une planification préopératoire précise des trajectoires des outils chirurgicaux est un facteur clé pour une intervention réussie. Cependant, une planification efficace est une tâche difficile, qui peut être considérablement améliorée en considérant différents facteurs contributifs tels que les déformations biomécaniques intra-opératoires, ou en introduisant de nouvelles techniques d'optimisation. Dans ce travail, nous nous concentrons sur deux aspects. Le premier aspect porte sur l'intégration de la déformation intra-opératoire dans le processus de planification de trajectoire. Nos méthodes combinent des techniques d'optimisation géométrique à base de simulations biomécaniques. Elles sont caractérisées par un certain niveau de généralité, et ont été expérimentées sur deux types d’interventions chirurgicales: les procédures percutanées pour l'ablation de tumeurs hépatiques, et la stimulation cérébrale profonde en neurochirurgie. Deuxièmement, nous étudions, mettons en œuvre, et comparons plusieurs approches d'optimisation en utilisant des méthodes qualitatives et quantitatives, et nous présentons une méthode efficace d'optimisation évolutionnaire multicritères à base de Pareto qui permet de trouver des solutions optimales qui ne sont pas accessibles par les méthodes existantes. / In image-guided minimally invasive surgery, a precise preoperative planning of the surgical tools trajectory is a key factor to a successful intervention. However, an efficient planning is a challenging task, which can be significantly improved when considering different contributing factors such as biomechanical intra-operative deformations, or novel optimization techniques. In this work, we focus on two aspects. The first aspect addresses integrating intra-operative deformation to the path planning process. Our methods combine geometric-based optimization techniques with physics-based simulations. They are characterized with a certain level of generality, and are experimented on two different surgical procedures: percutaneous procedures for hepatic tumor ablation, and in neurosurgery for Deep Brain Stimulation (DBS). Secondly, we investigate, implement, and compare many optimization approaches using qualitative and quantitative methods, and present an efficient evolutionary Pareto-based multi-criteria optimization method which can find optimal solutions that are not reachable via the current state of the art methods.

Objectif: Lors de l’ablation de tumeurs cérébrales, la navigation chirurgicale est basée sur les examens IRM pré-opératoires. Or, la déformation per-opératoire du cerveau, appelée brain-shift, affecte cette navigation. Dans cette thèse, une méthode de compensation du brain-shift intégrable dans un processus clinique est présentée.Méthode: Avant la chirurgie, un modèle biomécanique patient-spécifique est construit à partir des images pré-opératoires. Il intègre la géométrie des tissus mous mais également des vaisseaux. Pendant l’opération, des acquisitions échographiques localisées sont réalisées directement en contact avec le cerveau. Les modalités mode B et Doppler sont enregistrées simultanément, permettant respectivement l’extraction des vaisseaux et de l’empreinte de la sonde. Une simulation biomécanique est ensuite jouée pour compenser le brain-shift. Différentes contraintes sont appliquées au modèle de cerveau afin de modéliser les contacts avec la dure-mère, recaler les vaisseaux pré- et per-opératoires et contraindre la surface corticale avec l’empreinte de la sonde. Lors de la résection de tumeurs profondes, la trajectoire chirurgicale est également contrainte au sein de la cavité réséquée afin de retrouver les déformations latérales induites par l’écartement des tissus. Les images IRM pré-opératoires ont finalement mises à jour suivant le champ de déformation du modèle biomécanique.Résultats: La méthode a été évaluée quantitativement à partir de données synthétiques et cliniques de cinq patients. De plus, l’alignement des images a également été apprécié qualitativement, au regard des attentes des neurochirurgiens. Des résultats très satisfaisants, de l’ordre de 2 mm d’erreur, sont obtenus à l’ouverture de la dure-mère et dans le cas de résection de tumeurs en surface. Lors de la résection de tumeurs profondes, si la trajectoire chirurgicale permet de retrouver une grande partie des déformations induites par l’écartement des tissus, plusieurs limitations dues au fait que cette rétraction ne soit pas effectivement simulée sont montrées.Conclusion: Cette thèse propose une nouvelle méthode de compensation du brain-shit efficace et intégrable au bloc opératoire. Elle aborde de plus le sujet peu traité de la résection, en particulier de tumeurs profondes. Elle présente ainsi une étape supplémentaire vers un système optimal en neurochirurgie assistée par ordinateur. / Purpose: During brain tumor surgery, planning and guidance are based on preoperative MR exams. The intraoperative deformation of the brain, called brain-shift, however affect the accuracy of the procedure. In this thesis, a brain-shift compensation method integrable in a surgical workflow is presented.Method: Prior to surgery, a patient-specific biomechanical model is built frompreoperative images. The geometry of the tissues and blood vessels is integrated. Intraoperatively, navigated ultrasound images are performed directly in contact with the brain. B-mode and Doppler modalities are recorded simultaneously, enabling the extraction of the blood vessels and probe footprint, respectively. A biomechanical simulation is then executed in order to compensate for brain-shift. Several constraints are imposed to the biomechanical model in order to simulate the contacts with the dura mater, register the pre- and intraoperative vascular trees and constrain the cortical surface with the probe footprint. During deep tumors resection, the surgical trajectory is also constrained to remain inside the cavity induced by the resected tissues in order to capture the lateral deformations issued from tissues retraction. Preoperative MR images are finally updated following the deformation field of the biomechanical model.Results: The method was evaluated quantitatively using synthetic and clinical data. In addition, the alignment of the images was qualitatively assessed with respect to surgeons expectations. Satisfactory results, with errors in the magnitude of 2 mm, are obtained after the opening of the dura mater and for the resection of tumors close to the cortical surface. During the resection of deep tumors, while the surgical trajectory enable to capture most of the deformations induced by tissues retraction, several limitations reflects the fact that this retraction is not actually simulated.Conclusion: A new efficient brain-shift compensation method that is integrable in an operating room is proposed in this thesis. The few studied topic of the resection, and more specifically of deep tumors, is also addressed. This manuscript thus present an additional step towards an optimal system in computer assisted neurosurgery.

Dans cette thèse, nous avons développé des outils pour simuler des imageslongitudinales réalistes de cerveau présentant de l’atrophie ou de lacroissance. Cette méthode a été spécifiquement élaborée pour simuler leseffets de la maladie d’Alzheimer sur le cerveau. Elle se fonde sur un modèle dedéformation du cerveau qui décrit les effets biomécaniques d’une perte detissue due à une carte d’atrophie prescrite. Nous avons élaboré une méthodepour interpoler et extrapoler les images longitudinales d’un patient en simulantdes images avec une carte d’atrophie spécifique au sujet. Cette méthode a étéutilisée pour interpoler des acquisitions temporelles d’Images par RésonnanceMagnétique (IRM) de 46 patients souffrant de la maladie d’Alzheimer. Pour cefaire, des cartes d’atrophie sont estimées pour chaque patient, d’après deuxacquisitions IRM temporelles distinctes. Les IRM cliniques présentent du bruitet des artefacts. De plus, les acquisitions longitudinales présentent desvariations d’intensité d’une image à l’autre. Nous avons donc élaboré uneméthode qui combine le modèle de déformation du cerveau, ainsi que lesdifférentes images cliniques disponibles d’un patient donné, afin de simuler lesvariations d’intensité des acquisitions longitudinale. Pour finir, les outils desimulation d’images réalistes développés au cours de cette thèse sont mis àdisposition en open-source. / This thesis develops a framework to simulate realistic longitudinal brainimages with atrophy (and potentially growth), particularly in the case ofAlzheimer's Disease (AD). The core component of the framework is a braindeformation model: a carefully designed biomechanics-based tissue loss modelto simulate the deformations having the prescribed atrophy. The thesispresents a method to interpolate or extrapolate longitudinal images of asubject by simulating images with subject-specific atrophy patterns. Themethod was used to simulate interpolated time-point Magnetic ResonanceImages (MRIs) of 46 AD patients by prescribing atrophy estimated for eachpatient from the available two time-point MRIs. Real MRIs have noise andimage acquisition artefacts, and real longitudinal images have variation ofintensity characteristics among the individual images. In this thesis, wepresent a method that uses our brain deformation model and different availableimages of a subject to add realistic variations of intensities in the syntheticlongitudinal images. Finally, the software developed during the thesis tosimulate realistic longitudinal brain images with our brain deformation modelis released open-source.

Bone loss and muscle atrophy are two main physiological conditions affecting astronauts while being in space. In order to counteract the effects, at least two hours of aerobic and resistant countermeasure exercise is scheduled into their working day, seven days a week. Yoyo Technology AB has developed a resistance exercise device based on the flywheel principle, providing a load independent of gravity. However, there is no biomechanical research done on the efficiency of the device in microgravity, from a human movement point of view using simulation software. The aim of this thesis was to evaluate the effects of performing a leg press on the flywheel exercise device in a microgravity environment. Simulations of performing a flywheel leg press in earth gravity, microgravity and performing a conventional squat were done. The evaluated parameters were reaction forces, joint angles, joint moments, joint powers and muscle recruitment in the lower extremities. The simulations were done using a biomechanical simulation software based on a motion capture data collection. From the results two conclusions were proposed. Performing a flywheel leg press in microgravity environment or on earth provides at least as much peak moment as a body weighted squat performed on earth. Furthermore, performing a flywheel leg press in microgravity will induce a higher activity level among hip extensors and knee flexors compared to performing a flywheel leg press on earth.