Browsing by Author "Schmitt, Syn (Prof. Dr.)"

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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
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
Engineered bioinspired natural dynamics and their synergy with control and learning in legged robots
(2022) Ruppert, Felix; Schmitt, Syn (Prof. Dr.)
The performance of legged locomotion relies on the successful mitigation of unstructured, rough terrain in the presence of sparse information and neurosensory delays. Bioinspired walking systems benefit from carefully engineered passive compliant behavior that models the inherent elastic behavior of muscle-tendon structures in animals. To leverage the passive behavior that provides energy efficiency, passive stability as well as simplified control and learning tasks to the system, locomotion control and learning algorithms have to be designed and coordinated with the natural system dynamics in mind to achieve similar locomotion behavior we see in animals. The major contribution of this thesis is the synergy of a bio-inspired leg design with biarticular muscle-tendon structures, a wearable force and pressure sensor design for closed-loop control in legged locomotion, a biologically inspired closed-loop central pattern generator with reflex-like feedback and a learning approach that enables the locomotion controller to leverage the carefully engineered natural dynamics of the robot to learn convincing locomotion skills and increase energy efficiency. The first contribution is a biologically inspired leg design focusing on the biarticular lower leg muscle-tendon structure in vertebrate animals. The biarticular elasticity provides two-dimensional passive impedance to the leg and allows the storage of energy orthogonal to the leg axis direction. The leg blueprint is characterized in its capability to store and release elastic energy in the biarticular structure. The stored energy can be recuperated back into the system and increases the energy efficiency of the leg. This leg design achieves the lowest relative cost of transport documented for all dynamically hopping and running robots. The second contribution introduces the concept of training wheels, temporary mechanical modifications to the system dynamics that shape the learning reward landscape and simplify learning locomotion directly in hardware. Through deliberate changes to the system dynamics, in this case, reduced gravity, the reward landscape can be shaped to simplify the learning process. Learning with this training wheel is safer due to smoother reward landscapes with shallower gradients. Also, the initial guess for initiating the machine learning algorithm is simplified, because the salient gradient set of viable reward signals is bigger. During the learning process, the training wheel influence can be gradually reduced and the learning algorithm converges to the solution of the initial learning problem without training wheels. The third contribution presents a rugged, lightweight force and pressure sensor for feedback information and biomechanical analysis. The sensor can be mounted on a robotic foot and provides continuous force and pressure feedback during locomotion in unstructured and soft terrain. The sensor is based on a pressure sensor, encapsulated in a polyurethane dome with an air cavity around the pressure sensor. External forces deform the sensor dome and the rising pressure in the air cavity is measured by the pressure sensor. Based on the dome geometry and material, the sensor range can be adjusted for different load cases. The sensor can be used in arrays to measure pressure distributions as well as a wearable force sensor in wet or granular media where classical force plates can not be utilized. The final contribution synergizes the individual contributions into a neuroinspired learning approach that matches a bioinspired closed-loop central pattern generator with reflex-like neuroelastic feedback to the natural dynamics of a quadruped robot with biarticular legs. Through sparse contact feedback from the foot sensor, the bioinspired central pattern generator can neuroelastically mitigate short-term perturbations to adapt the robot to its environment. Because the robot dynamics and the control task dynamics initially do not match, the controller uses the neuroelastic feedback to minimize the discrepancy between commanded and measured robot behavior. The amount of feedback activity during level walking can be used as a proxy to estimate the amount of dynamics mismatching. By minimizing the amount of required neuroelastic feedback the robot learns to neuroplastically match its control task dynamics to its natural dynamics through Bayesian optimization. Through the synergy of mechanics and control the biomechatronic system benefits from both the individual functionality of its components as well as their interplay during locomotion. The designed natural dynamics provide advantageous passive behavior to the robot and the bioinspired controller learns to leverage the natural dynamics to achieve convincing locomotion.
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.