Universität Stuttgart

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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.
Open Access
Biophysical validity of reduced soft tissue modelling in neuro-musculoskeletal simulations
(Stuttgart : Institut für Modellierung und Simulation Biomechanischer Systeme, Computational Biophysics and Biorobotics, 2024) Hammer, Maria; Schmitt, Syn (Prof. Dr. rer. nat.)
In the past decades, neuro-musculoskeletal simulations have become a key technology in biomechanical research, and are increasingly utilised to support clinical decision-making processes, evaluate occupational safety, and facilitate the design of assistive devices. The importance of precise and physiologically valid simulations cannot be emphasised enough across all application fields. However, achieving this accuracy becomes particularly challenging when developing reduced descriptions of soft tissue compartments, where the degrees of freedom and structural complexity are condensed into predominantly phenomenological or homogenised sub-models. Furthermore, it necessitates a deep understanding of the dynamic interplay among different soft tissue elements in the body, which, in turn, requires a high level of reliability and confidence in the employed underlying model. In order to ensure the usefulness of the models, it is crucial to strike a balance between the level of detail and the limitations arising from simplifications. This involves considering various possibilities to validate the mechanical behaviour of each sub-model individually and the overall model as a whole. The scientific aim of this dissertation is to investigate the load sharing of soft tissue compartments at the example of the human lumbar spine during active motions by using predictive simulations. To set the basis for this kind of research, the main objective is to create, calibrate, verify and validate a detailed neuro-musculoskeletal model, which gives rise to three research questions that guide this thesis: (1) How can (and should) common approaches for reduced single tissue models be improved to increase the level of biomechanical validity and physical verification of both the sub-models themselves, and the multibody models composed of them? (2) Which sub-structures are of biomechanical relevance for estimating internal forces and torques on a full-body scale? (3) Which validation methods need to be considered during the development of a physiological spine model, and how can the corresponding simulation results be assessed? This thesis encompasses five journal articles studies, contributing to the different aspects of the three research questions. More precisely, Contribution I addresses research questions (1) and (3) regarding how to enhance biomechanical validity of muscle routing in multibody models by introducing a novel algorithm for redirecting muscle paths. With this method, the physiological accuracy of muscle length and moment arm representations within Hill-type models can be refined, particularly for muscles spanning multiple joints with multiple degrees of freedom. Contribution II focuses on the intuitive assessment of the relative motion between two vertebral bodies. Using a newly developed method for graphical representation of finite helical axes, which fully encapsulates the information about rotation and translation of the relative motion between two vertebral bodies, extensive data sets were effectively presented through clustering methods. This work, thereby, contributes to research question (3) since the finite helical axis can serve as measure for model validation. Contribution III presents a comprehensive simulation study and introduces a detailed generic model of the human thoracolumbar spine. This generic model includes several hundred muscle and ligament strands, along with intervertebral joints modelled as free joints constrained only by intervertebral discs. Notably, the model is able to balance gravity in an upright position without additional constraints and with only a physiologically low level of muscle stimulation. The description of model development and validation significantly contributes to research question (3). Moreover, the analysis of load sharing among the three soft tissue sub-structures, namely ligaments, muscles, and intervertebral discs, revealed an almost equal distribution of bending moment during forward flexion, offering insights for research question (2). Additionally, this paper introduces a workflow for geometric individualisation of the generic model based on landmark data obtained from computed-tomography scans. The investigation of subject-specific forces and torques exhibits significant inter- and intrapersonal differences in the lumbar load distribution. These findings deepen the biomechanical understanding of complex interactions within the spine. Contribution IV explores the influence of neglecting entire passive tissue groups, precisely intervertebral discs and ligaments, which is common practice in many spine models. Using an inverse dynamic approach, this study contributes to research questions (2) and (3) by providing cross-platform validation. The examination of kinematic models that exclude ligament and intervertebral disc tissues reveals a tendency for highly overestimated muscle forces. These findings highlight the importance of considering all relevant soft tissue structures in computational models to ensure accurate muscle force estimations. Lastly, the Contribution V directly addresses research question (1) by tackling the challenge of incorporating energy conservation in surrogate models representing the elastic mechanical responses of intervertebral discs. It introduces a novel approach that surpasses currently used elastic models in terms of precision, coupling of different degrees of freedom, and nonlinear behaviour. This advancement increases the accuracy and physical validity while also enabling the individualisation of the mechanical behaviour of subject- and level-specific intervertebral disc geometry. In order to achieve the aforementioned aims and answer the research questions, a multibody simulation framework has been extended by the novel algorithms developed within the scope of this thesis. These algorithms improve the geometric quality of soft tissue representations and refine accuracy of predicted internal forces and torques while preserving basic physical principles. Furthermore, a pre-processor typically used to scale population-based models has been modified to serve two additional purposes. On the one hand, the existing code was advanced to create geometrically individualised models based on landmark positions derived from computed-tomography scans. On the other hand, the functionality to generate models compatible with another widely used multibody simulation tool was included. Having identical models facilitates cross-platform validation, and allows to test muscle, ligament and intervertebral disc sub-models, and whole thoracolumbar spine models in different scenarios. In summary, this thesis enhances existing methods and provides new approaches to create more accurate neuro-musculoskeletal models capable of predicting internal forces and kinematics. The detailed spine model developed during this thesis serves as a valuable foundation for future investigations, including subject-specific, non-invasive implant testing, and exploration of the rotation axes in complex movements and post-surgical scenarios.
Open Access
Control framework for muscle-driven systems : exploiting bi-articular muscles in antagonistic setups to reduce control complexity and solve the muscle redundancy problem
(Stuttgart : Institut für Modellierung und Simulation Biomechanischer Systeme, 2022) Wolfen, Simon; Schmitt, Syn (Prof. Dr.)
Pneumatische Muskelaktuatoren und deren Verwendung in biorobotischen Systemen (Muskelgetriebene Roboter) stellen auf Grund ihrer Eigenschaften (Nicht-lineares Verhalten, Hysterese, monodirektionale Wirkungsrichtung, etc.) eine besondere Herausforderung an einen Regler. Während etablierte Regelstrategien wie Modellbasierte Regelungen oder KI-basierte Regelungen zwar in der Lage sind, muskelgetriebene Robotersysteme mit wenigen Gelenken und wenigen mono-artikularen Muskeln zu handhaben, scheitern diese Ansätze an der Skalierbarkeit (Erweiterung) von weiteren Muskel-Aktuatoren und Gelenken. Besonders bi-artikulare Muskeln in solchen bio-inspirierten Robotersystemen lassen sich mit den etablierten Regelstrategien nur mit einer Steigerung der Komplexität (bei Modell-basierten Regelungen) oder Datenquantität (KI-basierten Regelungen) meistern. Dies liegt daran, dass diese Ansätze zwar Lösungen zu den bekannten „Problemen“ wie Multi-Redundanz von Aktuatoren oder bi-artikulare Muskeln allgemein bieten, jedoch diese generell als Problem definieren, anstatt ihre Eigenschaften zu nutzen. In dieser Arbeit wird ein alternativer Regelungsansatz vorgestellt, der die nativen Eigenschaften von Muskel-Feder Aktuator Systemen nutzt, welche eine technische Repräsentation des biologischen Muskel Sehnen Komplexes darstellt. Dieser Regelungsansatz besitzt ein mathematisches Regler Modell, ohne jedoch ein mathematisches Aktuator Modell. Durch die Nutzung der systemischen Eigenschaften von bi-artikularen Muskeln in Gelenknetzwerken löst er das Skalierungsund Parameterproblem. Durch die geometrischen Eigenschaften der Gelenk-Netzwerke können wenige zu bestimmende Parameter auf alle Muskeln des Gesamtsystems angewendet werden. Das vorgestellte Regelsystem stellt daher in einer bio-inspirierten Regler Hierarchie die unterste Regler Schicht dar, jene, welche aus Gelenk Positionssollwerten zugehörige Muskelkommandos generiert. Dieses Regler System wird an Hand von zwei robotischen Systemen untersucht und die Regler Leistung als Zeit in der eine stabil Position mit einer bestimmten resultierenden Regel Abweichung (Genauigkeit) resultiert, definiert. Dieses Regelsystem fokussiert sich damit darauf, anwendbare robotische Systeme in Echtzeit unter biologischen Gegebenheiten wie Sensorverzögerung zu Regeln. Das Regelsystem stellt nicht den Anspruch an sich eine Replikation des biologischen Regelsystems zu sein.
Open Access
From muscle spindle to spinal cord : a modelling approach of the hierarchical organization in sensorimotor control
(Stuttgart : Institut für Modellierung und Simulation Biomechanischer Systeme, Computational Biophysics and Biorobotics, 2025) Santana Chacon, Pablo Filipe; Schmitt, Syn (Prof. Dr. rer. nat.)
The muscle spindle is an essential proprioceptor, significantly involved in sensing limb position and movement. Although biological spindle models exist for years, the gold-standard for motor control in biomechanics are still sensors built of homogenized spindle output models due to their simpler combination with neuro-musculoskeletal models. The performance of new studies that consider different structures of the hierarchical sensorimotor control system, implementing physiologically-motivated neuromechanical models aligned to proprioception, is essential to enable a more holistic understanding about movement. The incorporation of more biological proprioceptive and neuronal circuit models to muscles can make neuro-musculoskeletal systems more appropriate to investigate and elucidate motor control. Therefore, initially, this doctoral dissertation presents a more physiological model of the muscle spindle that considers the individual characteristics of involved tissue compartments, aligned to the advantage of easy integration into large-scale musculoskeletal models. Different stretches in the intrafusal fibers were simulated in the model's variations following the spindle afferent recorded in previous experiments in feline soleus muscle. Additionally, the proposed enhanced Hill-type spindle models had their parameters extensively optimized to match the experimental conditions, and the resulting model was validated against data from rats’ triceps surae muscle. As result, the model exhibits a stable and valid prediction of experimentally observed muscle spindle responses. At the same time, it presents a well-tuned Hill-type model as muscle spindle fibers – accounting for real sarcomere force-length and force-velocity aspects - and its activation dynamics is similar to the one applied to Hill-type model for extrafusal fibers, making it more easily integrated in multi-body simulations. Furthermore, this dissertation aims to demonstrate that the afferent firings from the muscle spindle model can be processed by neuronal networks and are important for motor control. Hence, the spindle model was integrated to a previous implemented extrafusal fiber model, inside of the demoa multi-body simulation framework. This structure composed by extrafusal (muscle) and intrafusal (spindle) fibers replaced the muscle-tendon units (MTUs) of a prior developed arm model composed by two degrees of freedom and six MTUs, into the same simulation framework. Additionally, a spinal cord model, based on literature, was implemented in the Nest spiking neural network simulator. The spinal network has 6 neurons per muscle - alpha, dynamic gamma and static gamma motoneurons, together with Ia, propriospinal and Renshaw interneurons – and their respective physiological connections. The coupling between demoa and Nest simulators was implemented using a Cython interface. The spinal cord network - in its two variations of complete and simpler circuitry (including only Renshaw pathway, without spindle proprioception) - had its synaptic weights optimized to perform a center-out reaching task using the musculoskeletal model, without and with perturbation (increment of lower arm segment in 1 kg). As result, the complete spinal cord circuitry learned how to successfully reach all the evaluated targets without and with perturbation, demonstrating the sensorimotor control learning in the environment formed from muscle spindle to spinal circuitry, encompassing the two simulators. On the other hand, the simpler spinal cord circuitry did not succeed in the task of reach all the targets, also demonstrating reduced performance with perturbation. Moreover, the spindle afferent synapses in the complete circuitry were intensified for the higher targets (considered more difficult under gravity) when comparing the scenarios without and with perturbation. Therefore, the muscle spindle connections were strengthened for difficult targets under perturbation, highlighting the importance of spindle proprioception in these more difficult scenarios, as well as indicated by the circuitry that does not consider proprioception and did not show a similar successful performance. Finally, this dissertation offers a novel possibility of neuro-musculoskeletal modelling environment formed with demoa and Nest simulators. Future outlook includes the integration of the musculoskeletal and spinal cord models with higher-level models of Central Nervous System, aligned to further sophisticated details of the current modelling, to allow a more comprehensive understanding of sensorimotor behavior.
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.
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.