Optimal control methods are applied to mechanical models in order to predict the control strategies in human arm movements. Optimality criteria are used to determine unique controls for a biomechanical model of the human upper-limb with redundant actuators. The motivation for this thesis is to provide a non-task-specific method of motion prediction as a tool for movement researchers and for controlling human models within virtual prototyping environments. The current strategy is based on determining the muscle activation levels (control signals) necessary to perform a task that optimizes several physical determinants of the model such as muscular and joint stresses, as well as performance timing. Currently, the initial and final location, orientation, and velocity of the hand define the desired task. Several models of the human arm were generated using a graph-theoretical method in order to take advantage of similar system topology through the evolution of arm models. Within this framework, muscles were modelled as non-linear actuator components acting between origin and insertion points on rigid body segments. Activation levels of the muscle actuators are considered the control inputs to the arm model. Optimization of the activation levels is performed via a hybrid genetic algorithm (GA) and a sequential quadratic programming (SQP) technique, which provides a globally optimal solution without sacrificing numerical precision, unlike traditional genetic algorithms. Advantages of the underlying genetic algorithm approach are that it does not require any prior knowledge of what might be a 'good' approximation in order for the method to converge, and it enables several objectives to be included in the evaluation of the fitness function. Results indicate that this approach can predict optimal strategies when compared to benchmark minimum-time maneuvers of a robot manipulator. The formulation and integration of the aforementioned components into a working model and the simulation of reaching and lifting tasks represents the bulk of the thesis. Results are compared to motion data collected in the laboratory from a test subject performing the same tasks. Discrepancies in the results are primarily due to model fidelity. However, more complex models are not evaluated due to the additional computational time required. The theoretical approach provides an excellent foundation, but further work is required to increase the computational efficiency of the numerical implementation before proceeding to more complex models.

Optimal control methods are applied to mechanical models in order to predict the control strategies in human arm movements. Optimality criteria are used to determine unique controls for a biomechanical model of the human upper-limb with redundant actuators. The motivation for this thesis is to provide a non-task-specific method of motion prediction as a tool for movement researchers and for controlling human models within virtual prototyping environments. The current strategy is based on determining the muscle activation levels (control signals) necessary to perform a task that optimizes several physical determinants of the model such as muscular and joint stresses, as well as performance timing. Currently, the initial and final location, orientation, and velocity of the hand define the desired task. Several models of the human arm were generated using a graph-theoretical method in order to take advantage of similar system topology through the evolution of arm models. Within this framework, muscles were modelled as non-linear actuator components acting between origin and insertion points on rigid body segments. Activation levels of the muscle actuators are considered the control inputs to the arm model. Optimization of the activation levels is performed via a hybrid genetic algorithm (GA) and a sequential quadratic programming (SQP) technique, which provides a globally optimal solution without sacrificing numerical precision, unlike traditional genetic algorithms. Advantages of the underlying genetic algorithm approach are that it does not require any prior knowledge of what might be a 'good' approximation in order for the method to converge, and it enables several objectives to be included in the evaluation of the fitness function. Results indicate that this approach can predict optimal strategies when compared to benchmark minimum-time maneuvers of a robot manipulator. The formulation and integration of the aforementioned components into a working model and the simulation of reaching and lifting tasks represents the bulk of the thesis. Results are compared to motion data collected in the laboratory from a test subject performing the same tasks. Discrepancies in the results are primarily due to model fidelity. However, more complex models are not evaluated due to the additional computational time required. The theoretical approach provides an excellent foundation, but further work is required to increase the computational efficiency of the numerical implementation before proceeding to more complex models.

This project presented a biomechanical model of the lumbosacral spine and pelvis, including novel methodologies associated with the measurement of human mechanics. This research has, for the first time, produced accurate three-dimensional geometric models of the human skeleton from living subjects using magnetic resonance imaging technology, enabling the prediction of physiological muscle action within individuals. The model was used to examine changes in the mechanics of the lumbopelvic musculoskeletal system between the standing and seated postures due to the increasing prevalence of the seated posture in the work and home environment. The outcomes of this research included a novel bone wrapping algorithm used to describe the effect of muscle-bone interactions. a novel method for creating three-dimensional in vivo spinal reconstructions using MRI, three dimensional in vivo helical axis measurements and subject specific normalised moment data.

A digital twin project is launched by the Integrative Systems Biology (ISB) research team and led by Gunnar Cedersund. The digital twin project is based on biological models of physiological processes, that can interact and be tailored for a specific person. However, the digital twin can currently not analyse movements of a human body. In this master thesis, the aim was to create a useful pipeline that expands the digital twin project with biomechanical modelling of movements, and also visualises the twins by letting the concept take human form. The biomechanical analysis was done in the software OpenSim, where the movements of a motion captured dance were analysed. To generate a simulation of the motion with an acceptable error in a reasonable computation time, a musculoskeletal model was created in OpenSim and scaled to best fit the anthropometry of the dancer. Then, the motion was estimated with an optimised procedure by using the scaled model and the motion capture data. The Root-Mean Squared (RMS) error of the estimated dance with accuracy 10-6 was 2.39 cm. In this thesis, the torque in each joint for the dance motion was estimated. The loads and muscle forces can also be estimated in OpenSim. One useful application is for calculating energy consumption. In order to calculate muscle forces, external forces needs to be measured while recording motion capture. This is something that will be focused on in the future, when continuing with this project. The visualisation of the digital twins were made in Unreal Engine with MetaHuman avatars. The dance recorded in motion capture, were applied to the avatars in order to make them dance. The recorded dance was the same for both OpenSim and Unreal Engine, so the dance could both be viewed and analysed. In conclusion, we have added a new feature to the existing digital twin technology: movements and simulation of the musculoskeletal system. This new feature can in the future be used for both medical purposes such as movement-based rehabilitation as well as for integration into dance performances.

L'arthrose de la main est une pathologie qui engendre des douleurs et des impotences fonctionnelles fortement handicapantes pour la vie quotidienne. Malheureusement, du fait de la complexité biomécanique de la main et du manque de quantification des forces subies par les articulations des doigts, la prévention et la réhabilitation de cette pathologie demeurent problématiques. L'objectif de ce travail doctoral a été de développer une modélisation musculo-squelettique de la main pour améliorer la compréhension de l'arthrose du point de vue biomécanique. Un modèle complet de la main, incluant les cinq doigts et le poignet, ainsi qu'un protocole expérimental de mesure de la cinématique et des forces externes appliquées à la main ont d'abord été développés pour estimer l'ensemble des forces musculaires et des forces articulaires durant la préhension. Ces outils méthodologiques ont permis de clarifier les risques d'arthrose associés aux types de préhension ainsi que ceux spécifiques aux articulations. Afin d'analyser plus précisément les facteurs de risques associés à chaque individu, une méthode d'individualisation des paramètres musculaires a été développée afin de mettre le modèle de la main à l'échelle des capacités réelles des individus. Cette méthode a été employée pour l'analyse de deux patientes et a permis de caractériser les adaptations et les conséquences biomécaniques associées à leurs affections spécifiques. Le modèle de la main et les protocoles expérimentaux développés ont ainsi fournit des données quantifiées qui représentent un intérêt concret pour l'amélioration de la prévention ainsi que pour l'élaboration et l'évaluation de programmes de réhabilitation. / Hand osteoarthritis is a pathology which results in pain and functional impotencies which are problematic for everyday life. Unfortunately, because of the complexity of hand biomechanics and the lack of quantification of finger joint loadings, the prevention and the rehabilitation of this pathology remain problematic. The objective of this doctoral work was to develop the musculoskeletal modelling of the hand to improve the understanding of hand osteoarthritis from a biomechanical point of view. A complete model of the hand, including the five fingers and the wrist, as well as an experimental protocol for measuring hand kinematics and grip forces were first developed to estimate all the muscle forces and joint forces during prehension tasks. These methodological tools have then been used to clarify the risk factors of hand osteoarthritis associated to prehension tasks and to specific joints. To investigate more precisely the risk factors associated to individuals, a method has been developed to individualise muscle parameters of the hand musculoskeletal model in order to provide a better representation of the real performances of each subject. This method has then been applied to the analysis of two osteoarthritis patients and allowed a complete characterization of the specific biomechanical adaptations and consequences associated to their specific affections. The hand musculoskeletal model and the experimental protocols developed during this doctoral work provided quantified data which represents a concrete interest to improve prevention but also to elaborate and evaluate rehabilitation programs.

Motion capture has in recent years grown in interest in many fields from both game industry to sport analysis. The need of reflective markers and expensive multi-camera systems limits the business since they are costly and time-consuming. One solution to this could be a deep neural network trained to extract 3D joint estimations from a 2D video captured with a smartphone. This master thesis project has investigated the accuracy of a trained convolutional neural network, MargiPose, that estimates 25 joint positions in 3D from a 2D video, against a gold standard, multi-camera Vicon-system. The project has also investigated if the data from the deep neural network can be connected to a biomechanical modelling software, AnyBody, for further analysis. The final intention of this project was to analyze how accurate such a combination could be in golf swing analysis. The accuracy of the deep neural network has been evaluated with three parameters: marker position, angular velocity and kinetic energy for different segments of the human body. MargiPose delivers results with high accuracy (Mean Per Joint Position Error (MPJPE) = 1.52 cm) for a simpler movement but for a more advanced motion such as a golf swing, MargiPose achieves less accuracy in marker distance (MPJPE = 3.47 cm). The mean difference in angular velocity shows that MargiPose has difficulties following segments that are occluded or has a greater motion, such as the wrists in a golf swing where they both move fast and are occluded by other body segments. The conclusion of this research is that it is possible to connect data from a trained CNN with a biomechanical modelling software. The accuracy of the network is highly dependent on the intention of the data. For the purpose of golf swing analysis, this could be a great and cost-effective solution which could enable motion analysis for professionals but also for interested beginners. MargiPose shows a high accuracy when evaluating simple movements. However, when using it with the intention of analyzing a golf swing in i biomechanical modelling software, the outcome might be beyond the bounds of reliable results.

La modélisation du système musculo-squelettique est un outil permettant l'amélioration des connaissances du fonctionnement biomécanique des structures ostéo-articulaires et musculo- tendineuses. Nos travaux de recherche portent sur le développement d'une méthodologie de modélisation personnalisée, volumique, déformable et à capacité contractile du système musculo- squelettique du membre inférieur, intégrant l'ensemble des outils, le plus possible automatisés, de construction (basée sur l'imagerie médicale), de simulation (en couplage avec un modèle multi-corps dynamique) et d'analyse (comme la cartographie des raideurs locales dans le muscle) nécessaires à leur mise en œuvre dans le cadre d'études orthopédiques / Musculo-skeletal modeling can update our knowledge concerning the biomechanical behavior of the osteoarticular and musculotendinous structures. This research work is focus on the development of methodology and tools for the generation of a personalized model of the lower limb musculoskeletal system, taking account of the deformable and contractile behavior of the muscles. This workflow automatically builds the model dataset (from medical imagery), performs the simulations (coupled with a multibody dynamic model), and offers specific analysis tools (as local stiffness mapping in the active muscle) required for various orthopedic studies