Universität Stuttgart
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Item Open Access Data-driven modelling of neuromechanical adaptation in skeletal muscles in response to isometric exercise(Stuttgart : Institute for Modelling and Simulation of Biomechanical Systems, Chair of Continuum Biomechanics and Mechanobiology, University of Stuttgart, 2022) Altan, Neriman Ekin; Röhrle, Oliver (Prof., PhD)This study aims to model the changes in the behaviour of motor neurons of the vastus lateralis in response to unilateral isometric knee extension exercise (UIKEE). For this, the phenomenological motor control model by Fuglevand et al. (1993) has been used. Input parameters for this model have been calibrated against data from experimental studies available in literature by using Bayesian updating. The pre-exercise state of the motor neuron pool of the muscle describing the recruitment behaviour as well as the contractile properties of the motor neurons have been constructed. Data collected from a systematic review on the change in isometric strength due to UIKEE has been modelled using Bayesian lonigutidinal model-based meta-analysis. Using the model of the change in isometric strength, increase in the average motor neuron discharge rate following UIKEE has been quantified.Item Open Access Über die Regelung muskelgetriebener Systeme : ein hierarchischer und geometriebasierter Ansatz(Stuttgart : Institut für Modellierung und Simulation Biomechanischer Systeme, Computational Biophysics and Biorobotics, 2022) Walter, Johannes R.; Schmitt, Syn (Prof. Dr. rer. nat.)Computersimulationen sind heutzutage eine leistungsfähige wissenschaftliche Methode um Hypothesen unter simulierten Bedingungen zu überprüfen. Dennoch scheinen biologische Bewegungen von mehrgelenkigen Systemen mit einer Vielzahl von Muskeln das Ergebnis von neuronalen Kommandos zu sein, die zu komplex sind um algorithmisch implementiert zu werden. Daher ist die Vielfalt, sowie die Komplexität von in-silico synthetisierten, muskelgetriebenen Bewegungen noch immer gering. Ein Schlüsselproblem zur Regelung biologischer Bewegung ist es eine Verbindung zwischen einer konzeptionellen Idee der Bewegung und der Bereitstellung von Muskelstimulationen herzustellen. Dies kann sich als schwierig erweisen, da in biologischen Bewegungen die Anzahl der Muskeln größer ist als die Dimension des konzeptionellen Raums der Bewegungsidee, bspw. der mechanischen Freiheitsgraden (FHG) des Skelettsystems. In dieser Dissertation wird eine mathematische Formulierung einer hierarchischen Regelungsarchitektur vorgestellt, die eine solche Verbindung herstellt und die dazu ausgelegt ist eine Vielzahl von dreidimensionalen, muskelgetriebenen Bewegungen zu synthetisieren. Die Funktionsfähigkeit der Regelungsarchitektur ist anhand von verschiedenen menschlichen Bewegungsaufgaben demonstriert. Dies beinhaltet Simulationen von einem aufrechtem Stand, von einer Einstiegsbewegung in ein Fahrzeug, um ergonomische Rückschlüsse von einer virtuellen Designänderung zu ziehen, und von einem Sturz in eine Badewanne, um die Aufklärung eines Kriminalfalles zu unterstützen. Das zur Bewegungssynthese verwendete dreidimensionale digitale Menschmodell (DMM) besteht aus 20 Gelenk FHG und 36 Hill-Typ Muskel-Sehnen Einheiten (MSE). Das DMM ist erdähnlicher Gravitation ausgesetzt und die Füße interagieren mit dem Boden durch reversible Haft- und Gleitreibungskontakte. Die Regelungsarchitektur liefert kontinuierliche Stimulationen für alle MSE, basierend auf einer konzeptionellen Formulierung der Bewegungsaufgabe in den Koordinaten der Gelenkwinkel, der Gelenkmomente, der Positionen der Gliedmaßen oder in anderen konzeptionellen Koordinaten. Die Hierarchie der Regelungsarchitektur besteht aus drei Ebenen, der 'Konzeptionsebene', der 'Transformationsebene' und der 'Strukturebene'. In der 'Konzeptionsebene' wird die Bewegungsaufgabe in den konzeptionellen Koordinaten der Winkel, der Momente oder der Positionen formuliert und geregelt. Die Ausgangsgröße des konzeptionellen Reglers wird in einen Bewegungsplan für die Gelenkwinkel transformiert und bildet die Eingangsgröße für zwei Gelenkwinkelregler in der 'Transformationsebene'. Die 'Transformationsebene' kommuniziert mit den biologischen Strukturen in der 'Strukturebene', indem sie zum einen direkte Stimulationen für die MSE bereitstellt und zum anderen weitere Eingangssignale für strukturelle MSE Regler liefert. Dabei wird die Redundanz zwischen den MSE Stimulationen und den Gelenkwinkeln aufgelöst. Hierzu werden die Charakteristiken der modellierten biophysikalischen Strukturen, die Hebelarme der Muskeln, die Steifigkeitsverhältnisse innerhalb des Muskelmodells und die Längen-Stimulationsabhängigkeit der Aktivierungsdynamik, zu Nutze gemacht. Die von den MSE über ihre Hebelarme generierten Gelenkmomente beschleunigen die Körpersegmente und, indem die konzeptionellen Koordinaten an die Regler in der 'Konzeptionsebene' zurückgeführt werden, wird der hierarchische Regelkreis geschlossen. Die präsentierte Regelungsarchitektur erlaubt es damit eine konzeptionelle Bewegungsaufgabe direkt in Stimulationssignale der MSE zu übersetzen. Mit diesem Ansatz wird das Problem der Bewegungsplanung erleichtert, da bspw. nur das mechanische System in der konzeptionellen Planung betrachtet werden muss. Da zudem die Auflösung der Muskel-Gelenk-Redundanz nicht eindeutig ist, verbleibt zur Regelung eine 'ungeregelte Mannigfaltigkeit', mit der die Kokontraktion aller Muskeln an dem selben Gelenk genau so angepasst werden kann, dass sie nicht mit der Erfüllung der Bewegungsaufgabe in Konflikt steht. Die Ergebnisse dieser Dissertation sind vielversprechend bezüglich der Anwendung der Regelungsarchitektur für die Synthese von dynamischen und komplexen muskelgetriebenen Bewegungen, auch für robotische Systeme die mit künstlichen Muskeln ausgestattet sind. Die internen Zustände des muskuloskelettalen Models sind zu weiterführenden Analysen geeignet, wie z.B. zur Evaluation der Ergonomie oder zur Abschätzung gesundheitlicher Auswirkungen der Bewegung.Item Open Access On the load limits of the muscle-tendon unit and their applications in musculoskeletal human body models(Stuttgart : Institut für Modellierung und Simulation Biomechanischer Systeme, Computational Biophysics and Biorobotics, 2024) Nölle, Lennart V.; Schmitt, Syn (Prof. Dr.)The human skeletal muscle fulfils many movement-related functions, simultaneously acting as the main motor, spring, strut and brake of the body. Equally important for human motion generation are the tendons, which provide passive joint stabilisation and transfer the muscle’s contraction forces to the skeletal structure. Together, muscle and tendon form the muscle-tendon unit (MTU). Despite its ability to withstand many different loading scenarios, the MTU is susceptible to numerous kinds of injury, the most prevalent being the muscle strain injury. The retrospective evaluation of observed injury scenarios and the prediction of injury outcomes and risks has been increasingly important in sports biomechanics, automotive safety and forensic traumatology. For this purpose, numerous injury criteria have been defined for the use with both physical and virtual representations of the human body. While significant efforts in the field of injury severity classification have been made, strain injuries of the MTU have not yet been taken into consideration. This might be because conventional methods of defining injury criteria are not applicable to MTU strain injuries as the properties of the MTU and the nature of MTU strain injuries pose numerous unresolved challenges so far. The primary objective of this dissertation is to overcome these challenges and to define and substantiate MTU strain injury criteria for the use in musculoskeletal human body model simulations. The overarching research question which the presented thesis aims to answer is how injury criteria for strain injuries of the MTU can be defined and which information can be derived from their application. Throughout, the following sub-questions are addressed: 1. How can a strain injury criterion for the muscle be defined and substantiated based on literature data? 2. How can a strain injury criterion for the tendon be defined and applied to the recreation of an injury load case? 3. Which other applications besides injury severity assessment exist for the proposed injury criteria? These questions were tackled consecutively in three journal publications which comprise this dissertation. Sub-question 1 was answered in Contribution 1, where a muscle strain injury criterion (MSIC) was defined based on experimental data from the literature. The resulting injury criterion can differentiate between three levels of injury severity and is easily applicable to the computational representation of any muscle. The injury thresholds were substantiated by comparison to the calculated maximum ultimate tensile strength of mammalian skeletal muscle and through the application of the MSIC in a sprinting gait cycle simulation. The MSIC was also used for a simulation study on the aetiology of muscle strain injuries in which it was shown that material inhomogeneities might cause localised strain injuries within a muscle. To tackle sub-question 2, Contribution 2 built on the findings of Contribution 1 by formulating the tendon strain injury criterion TSIC. This criterion was used to investigate the forces and strains acting on finger flexor tendons during jersey finger injury scenarios. For this purpose, a finite element neuromusculoskeletal hand model was created through the combination of two preexisting models. Additionally, new Hill-type muscle elements were inserted whose parameters were calibrated to fit experimental data. The newly created hand model was used to recreate a simplified jersey finger injury load case under varying muscle activity levels. This simulations study showed that a correlation between muscle activity and sustained injury severity exists. Finally, Contribution 3 set out to answer sub-question 3 and to demonstrate the usefulness of the MSIC and TSIC for applications other than injury severity assessment. For this, common modelling issues present in musculoskeletal human body models were first recreated and then detected using the proposed criteria. First, the deformation of a finite element model’s skeletal structure during model repositioning was identified through an MSIC assessment of muscles spanning a displaced joint. Second, an ill-tuned muscle parameter within an otherwise physiological model was found through applying the TSIC to a multibody gait cycle simulation. Additionally, a new method for determining minor TSIC thresholds for arbitrarily parameterised tendons was developed, thus improving the usability of the TSIC. The cumulative result of this thesis is a strain injury criterion for the MTU which, to the author’s knowledge, is the first of its kind. Additionally, a new method for evaluating the quality of musculoskeletal human body models was provided. Future studies should focus on the experimental validation of the proposed injury criteria and on expanding them by statistical metrics. Potential application scenarios of the MSIC and TSIC, besides injury evaluation, are as model assessment tools or in ergonomics.Item Open Access The benefit of muscle-actuated systems : internal mechanics, optimization and learning(Stuttgart : Institut für Modellierung und Simulation Biomechanischer Systeme, Computational Biophysics and Biorobotics, 2023) Wochner, Isabell; Schmitt, Syn (Prof. Dr.)We are facing the challenge of an over-aging and overweight society. This leads to an increasing number of movement disorders and causes the loss of mobility and independence. To address this pressing issue, we need to develop new rehabilitation techniques and design innovative assistive devices. Achieving this goal requires a deeper understanding of the underlying mechanics that control muscle-actuated motion. However, despite extensive studies, the neural control of muscle-actuated motion remains poorly understood. While experiments are valuable and necessary tools to further our understanding, they are often limited by ethical and practical constraints. Therefore, simulating muscle-actuated motion has become increasingly important for testing hypotheses and bridge this knowledge gap. In silico, we can establish cause-effect relationships that are experimentally difficult or even impossible to measure. By changing morphological aspects of the underlying musculoskeletal structure or the neural control strategy itself, simulations are crucial in the quest for a deeper understanding of muscle-actuated motion. The insights gained from these simulations paves the way to develop new rehabilitation techniques, enhance pre-surgical planning, design better assistive devices and improve the performance of current robots. The primary objective of this dissertation is to study the intricate interplay between musculoskeletal dynamics, neural controller and the environment. To achieve this goal, a simulation framework has been developed as part of this thesis, enabling the modeling and control of muscle-actuated motion using both model-based and learning-based methods. By utilizing this framework, musculoskeletal models of the arm, head-neck complex and a simplified whole-body model are investigated in conjunction with various concepts of motor control. The main research questions of this thesis are therefore: 1. How does the neural control strategy select muscle activation patterns to generate the desired movement, and can we use this knowledge to design better assistive devices? 2. How does the musculoskeletal dynamics facilitate the neural control strategy in accomplishing this task of generating desired movements? To address these research questions, this thesis comprises a total of five journal and conference articles. More specifically, contributions I-III of this thesis focus on addressing the first research question which aims to understand how voluntary and reflexive movements can be predicted. First, we investigate various optimality principles using a musculoskeletal arm model to predict point-to-manifold reaching tasks. By using predictive simulations, we demonstrate how the arm would move towards a goal if, for example, our neural control strategy would minimize energy consumption. The main finding of this contribution shows that it is essential to include muscle dynamics and consider tasks with more openly defined targets to draw accurate conclusions about motor control. Through our analysis, we show that a combination of mechanical work, jerk and neuronal stimulation effort best predicts point-reaching when compared to human experiments. Second, we propose a novel method to optimize the design of exoskeleton power units taking into account the load cycle of predicted human movements. To achieve this goal, we employ a forward dynamic simulation of a generic musculoskeletal arm model, which is first scaled to represent different individuals. Next, we predict individual human motions and employ the predicted human torques to scale the electrical power units employing a novel scalability model. By considering the individual user needs and task demands, our approach achieves a lighter and more efficient design. In conclusion, our framework demonstrates the potential to improve the design of individual assistive devices. The third contribution focuses on predicting reflexive movements in response to sudden perturbations of the head-neck complex. To achieve this, we conducted experiments in which volunteers were placed on a table while supporting their heads with a trapdoor. This trapdoor was then suddenly released leading to a downward movement of the head until the reflexive reaction of the muscles stops the head from falling. We analyzed the results of these experiments, presenting characteristic parameters and highlighting differences between separate age and gender groups. Using this data, we also set up benchmark validations for a musculoskeletal head-neck model, including reflex control strategies. Our main findings are that there are large individual differences in reflexive responses between participants and that the perturbation direction significantly affects the reflexive response. Furthermore, we show that this data can be used as a benchmark test to validate musculoskeletal models and different muscle control strategies. While the first three contributions focus on the research question (1), contributions IV-V focus on (2) whether and how the musculoskeletal dynamics facilitate the learning and control task of various movements. We utilize a recently introduced information-theoretic approach called control effort to quantify the minimally required information to perform specific movements. By applying this concept, we can for example quantify how much biological muscles reduce the neuronal information load compared to technical DC-motors. We present a novel optimization algorithm to find this control effort and apply it to point-reaching and walking tasks. The main finding of this contribution is that the musculoskeletal dynamics reduce the control effort required for these movements compared to torque-driven systems. Finally, we hypothesize that the highly nonlinear muscle dynamics not only facilitate the control task but also provide inherent stability that is beneficial for learning from scratch. To test this, we employed various learning strategies for multiple anthropomorphic tasks, including point-reaching, ball-hitting, hopping, and squatting. The results of this investigation demonstrate that using muscle-like actuators improves the data-efficiency of the learning tasks. Additionally, including the muscle dynamics improves the robustness towards hyperparameters and allows for a better generalization towards unknown and unlearned perturbations. In summary, this thesis enhances existing methods to control and learn muscle-actuated motion, quantifies the control effort needed to perform certain movements and demonstrates that the inherent stability of the muscle dynamics facilitates the learning task. The models, control strategies, and experimental data presented in this work aid researchers in science and industry to improve their predictions in various fields such as neuroscience, ergonomics, rehabilitation, passive safety systems, and robotics. This allows us to reverse-engineer how we as humans control movement, uncovering the complex relationship between musculoskeletal dynamics and neural controller.