02 Fakultät Bau- und Umweltingenieurwissenschaften
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3
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Item Open Access Determination of muscle shape deformations of the tibialis anterior during dynamic contractions using 3D ultrasound(2024) Sahrmann, Annika S.; Vosse, Lukas; Siebert, Tobias; Handsfield, Geoffrey G.; Röhrle, OliverPurpose: In this paper, we introduce a novel method for determining 3D deformations of the human tibialis anterior (TA) muscle during dynamic movements using 3D ultrasound. Materials and Methods: An existing automated 3D ultrasound system is used for data acquisition, which consists of three moveable axes, along which the probe can move. While the subjects perform continuous plantar- and dorsiflexion movements in two different controlled velocities, the ultrasound probe sweeps cyclically from the ankle to the knee along the anterior shin. The ankle joint angle can be determined using reflective motion capture markers. Since we considered the movement direction of the foot, i.e., active or passive TA, four conditions occur: slow active, slow passive, fast active, fast passive. By employing an algorithm which defines ankle joint angle intervals, i.e., intervals of range of motion (ROM), 3D images of the volumes during movement can be reconstructed. Results: We found constant muscle volumes between different muscle lengths, i.e., ROM intervals. The results show an increase in mean cross-sectional area (CSA) for TA muscle shortening. Furthermore, a shift in maximum CSA towards the proximal side of the muscle could be observed for muscle shortening. We found significantly different maximum CSA values between the fast active and all other conditions, which might be caused by higher muscle activation due to the faster velocity. Conclusion: In summary, we present a method for determining muscle volume deformation during dynamic contraction using ultrasound, which will enable future empirical studies and 3D computational models of skeletal muscles.Item 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.Item Open Access Generation, probing, and biophysical stimulation of human microtissues in microfluidic Organ-on-Chip platforms(Stuttgart : Institute for Modelling and Simulation of Biomechanical Systems, Chair of Continuum Biomechanics and Mechanobiology, University of Stuttgart, 2022) Schneider, Oliver; Röhrle, Oliver (Prof., PhD)Over the last decade Organ-on-Chip (OoC) emerged as disruptive technology combining aspects of microfluidics and tissue engineering. OoCs culture human tissues in tailored microenvironments under microfluidic perfusion, yielding an unprecedented recapitulation of human physiology. So far, most systems predominantly focus on physiological tissue generation. However, it is crucial to integrate stimulation and readout capabilities, leveraging OoCs from bare tissue generation tools to advanced integrated experimental platforms. This thesis focuses on the development and characterization of novel microphysiological systems to probe and actuate tissues on the microscale. We present two Heart-on-Chip platforms enabling the generation of aligned cardiac muscle fibers and investigate the integration of force and O2 sensing as well as electrical stimulation capabilities. Furthermore, we introduce and characterize two OoCs enabling the precise delivery of biomechanical stretch and compression stimuli. All in all, the systems developed in the framework of this thesis provide a flexible toolkit amenable for disease modeling or personalized medicine, offering advanced experimental capabilities for manipulating and interrogating integrated tissues.Item Open Access The use of nonnormalized surface EMG and feature inputs for LSTM-based powered ankle prosthesis control algorithm development(2023) Keleş, Ahmet Doğukan; Türksoy, Ramazan Tarık; Yucesoy, Can A.Advancements in instrumentation support improved powered ankle prostheses hardware development. However, control algorithms have limitations regarding number and type of sensors utilized and achieving autonomous adaptation, which is key to a natural ambulation. Surface electromyogram (sEMG) sensors are promising. With a minimized number of sEMG inputs an economic control algorithm can be developed, whereas limiting the use of lower leg muscles will provide a practical algorithm for both ankle disarticulation and transtibial amputation. To determine appropriate sensor combinations, a systematic assessment of the predictive success of variations of multiple sEMG inputs in estimating ankle position and moment has to conducted. More importantly, tackling the use of nonnormalized sEMG data in such algorithm development to overcome processing complexities in real-time is essential, but lacking. We used healthy population level walking data to (1) develop sagittal ankle position and moment predicting algorithms using nonnormalized sEMG, and (2) rank all muscle combinations based on success to determine economic and practical algorithms. Eight lower extremity muscles were studied as sEMG inputs to a long-short-term memory (LSTM) neural network architecture: tibialis anterior (TA), soleus (SO), medial gastrocnemius (MG), peroneus longus (PL), rectus femoris (RF), vastus medialis (VM), biceps femoris (BF) and gluteus maximus (GMax). Five features extracted from nonnormalized sEMG amplitudes were used: integrated EMG (IEMG), mean absolute value (MAV), Willison amplitude (WAMP), root mean square (RMS) and waveform length (WL). Muscle and feature combination variations were ranked using Pearson’s correlation coefficient (r > 0.90 indicates successful correlations), the root-mean-square error and one-dimensional statistical parametric mapping between the original data and LSTM response. The results showed that IEMG+WL yields the best feature combination performance. The best performing variation was MG + RF + VM (rposition = 0.9099 and rmoment = 0.9707) whereas, PL (rposition = 0.9001, rmoment = 0.9703) and GMax+VM (rposition = 0.9010, rmoment = 0.9718) were distinguished as the economic and practical variations, respectively. The study established for the first time the use of nonnormalized sEMG in control algorithm development for level walking.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 Development and implementation of next-generation Retina-on-Chip platforms(Stuttgart : Institute for Modelling and Simulation of Biomechanical Systems, Chair for Continuum Biomechanics and Mechanobiology, University of Stuttgart, 2024) Chuchuy, Johanna; Röhrle, Oliver (Prof., PhD)Item Open Access Insights into human alpha-motoneuron discharge properties during stretch reflexes : an in-silico approach(Stuttgart : Institute for Modelling and Simulation of Biomechanical Systems, Chair of Continuum Biomechanics and Mechanobiology, University of Stuttgart, 2024) Schmid, Laura; Röhrle, Oliver (Prof., PhD)Item Open Access Multilevel convergence analysis : parallel-in-time integration for fluid-structure interaction problems with applications in cardiac flow modeling(Stuttgart : Institute for Modelling and Simulation of Biomechanical Systems, Chair of Continuum Biomechanics and Mechanobiology, University of Stuttgart, 2020) Hessenthaler, Andreas; Röhrle, Oliver (Prof., PhD)In this Ph.D. Thesis, multigrid-reduction-in-time (MGRIT) is considered as means to reduce the time-to-solution for numerical algorithms concerned with the solution of time-dependent partial differential equations (PDEs) arising in the field of fluid-structure interaction (FSI) modeling. As a parallel-in-time integration method, the MGRIT algorithm significantly increases the potential for parallel speedup by employing modern computer architectures, ranging from small-scale clusters to massively parallel high-performance computing platforms. In this work, the MGRIT algorithm is considered as a true multilevel method that can exhibit optimal scaling. Convergence of MGRIT is studied for the solution of linear and nonlinear (systems of) PDEs: from single- to multiphysics applications relevant to FSI problems in two and three dimensions. A multilevel convergence framework for MGRIT is derived that establishes a priori upper bounds and approximate convergence factors for a variety of cycling strategies (e.g., V- and F-cycles), relaxation schemes and parameter settings. The convergence framework is applied to a number of test problems relevant to FSI modeling, both linear and nonlinear as well as parabolic and hyperbolic in nature. An MGRIT variant is further proposed that exploits the time-periodicity that is present in many biomedical engineering applications, e.g., cyclic blood flow in the human heart. The time-periodic MGRIT algorithm proves capable of consistently reducing the time-to-solution of an existing simulation model with significant observed speedups.Item Open Access Bioelectromagnetic fields for studying neuromuscular physiology : in silico investigations of EMG and MMG(Stuttgart : Institute for Modelling and Simulation of Biomechanical Systems, Chair of Continuum Biomechanics and Mechanobiology, University of Stuttgart, 2023) Klotz, Thomas; Röhrle, Oliver (Prof., PhD)Skeletal muscles generate bioelectromagnetic fields that contain information about the neural control of motions and the function of the muscle. One distinguishes between electromyography (EMG), the measurement of the muscle-induced electric potential field, and magnetomyography (MMG), the recording of muscle-induced magnetic fields. EMG is a well-established methodology, and its limitations have been extensively discussed in the scientific literature. In contrast, MMG is an emerging methodology with the potential to overcome some of the inherent limitations of EMG. To unlock the full potential of MMG, it is essential to support empirical observations from experiments with a solid theoretical understanding of muscle-induced bioelectromagnetic fields. Therefore, this thesis derives a novel multiscale skeletal muscle model that can predict realistic EMG and MMG signals. This model is used to conduct the first systematic comparison between surface EMG and non-invasive MMG. By using simulations, all system parameters can be controlled precisely. This would not be possible experimentally. The fundamental properties of EMG and MMG are systematically explored using simulations comparable to electrically or reflex-evoked contractions. Notably, it is shown that non-invasive MMG data is spatially more selective than comparable high-density EMG data. This property, for example, is advantageous for decomposing signals of voluntary contractions into individual motor unit spike trains. Using a novel in silico trial framework, it is demonstrated that non-invasive MMG-based motor unit decomposition is superior to the well-established surface EMG-based motor unit decomposition.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.