02 Fakultät Bau- und Umweltingenieurwissenschaften
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3
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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.Item 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.Item Open Access Optimality principles in human point-to-manifold reaching accounting for muscle dynamics(2020) Wochner, Isabell; Driess, Danny; Zimmermann, Heiko; Häufle, Daniel F. B.; Toussaint, Marc; Schmitt, SynHuman arm movements are highly stereotypical under a large variety of experimental conditions. This is striking due to the high redundancy of the human musculoskeletal system, which in principle allows many possible trajectories toward a goal. Many researchers hypothesize that through evolution, learning, and adaption, the human system has developed optimal control strategies to select between these possibilities. Various optimality principles were proposed in the literature that reproduce human-like trajectories in certain conditions. However, these studies often focus on a single cost function and use simple torque-driven models of motion generation, which are not consistent with human muscle-actuated motion. The underlying structure of our human system, with the use of muscle dynamics in interaction with the control principles, might have a significant influence on what optimality principles best model human motion. To investigate this hypothesis, we consider a point-to-manifold reaching task that leaves the target underdetermined. Given hypothesized motion objectives, the control input is generated using Bayesian optimization, which is a machine learning based method that trades-off exploitation and exploration. Using numerical simulations with Hill-type muscles, we show that a combination of optimality principles best predicts human point-to-manifold reaching when accounting for the muscle dynamics.Item Open Access Giraffes and hominins: reductionist model predictions of compressive loads at the spine base for erect exponents of the animal kingdom(2021) Günther, Michael; Mörl, FalkIn humans, compressive stress on intervertebral discs is commonly deployed as a measurand for assessing the loads that act within the spine. Examining this physical quantity is crucially beneficial: the intradiscal pressure can be directly measured in vivo in humans, and is immediately related to compressive stress. Hence, measured intradiscal pressure data are utterly useful for validating such biomechanical animal models that have the spine incorporated, and can, thus, compute compressive stress values. Here, we utilise human intradiscal pressure data to verify the predictions of a reductionist spine model, which has in fact only one joint degree of freedom. We calculate the pulling force of one lumped anatomical structure that acts past this (intervertebral) joint at the base of the spine - lumbar in hominins, cervical in giraffes - to compensate the torque that is induced by the weight of all masses located cranially to the base. Given morphometric estimates of the human and australopith trunks, respectively, and the giraffe's neck, as well as the respective structures' lever arms and disc areas, we predict, for all three species, the compressive stress on the intervertebral disc at the spine base, while systematically varying the angular orientation of the species' spinal columns with respect to gravity. The comparison between these species demonstrates that hominin everyday compressive disc stresses are lower than such in big quadrupedal animals. Within each species, erecting the spine from being bent forward by, for example, thirty degrees to fully upright posture reduces the compressive disc stress roughly to a third. We conclude that erecting the spine immediately allows to carry extra loads of the order of body weight, and yet the compressive disc stress is lower than in a moderately forward-bent posture with none extra load.Item 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.