Universität Stuttgart
Permanent URI for this communityhttps://elib.uni-stuttgart.de/handle/11682/1
Browse
39 results
Search Results
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 Permeability estimation of regular porous structures : a benchmark for comparison of methods(2021) Wagner, Arndt; Eggenweiler, Elissa; Weinhardt, Felix; Trivedi, Zubin; Krach, David; Lohrmann, Christoph; Jain, Kartik; Karadimitriou, Nikolaos; Bringedal, Carina; Voland, Paul; Holm, Christian; Class, Holger; Steeb, Holger; Rybak, IrynaThe intrinsic permeability is a crucial parameter to characterise and quantify fluid flow through porous media. However, this parameter is typically uncertain, even if the geometry of the pore structure is available. In this paper, we perform a comparative study of experimental, semi-analytical and numerical methods to calculate the permeability of a regular porous structure. In particular, we use the Kozeny-Carman relation, different homogenisation approaches (3D, 2D, very thin porous media and pseudo 2D/3D), pore-scale simulations (lattice Boltzmann method, Smoothed Particle Hydrodynamics and finite-element method) and pore-scale experiments (microfluidics). A conceptual design of a periodic porous structure with regularly positioned solid cylinders is set up as a benchmark problem and treated with all considered methods. The results are discussed with regard to the individual strengths and limitations of the used methods. The applicable homogenisation approaches as well as all considered pore-scale models prove their ability to predict the permeability of the benchmark problem. The underestimation obtained by the microfluidic experiments is analysed in detail using the lattice Boltzmann method, which makes it possible to quantify the influence of experimental setup restrictions.Item Open Access Experiments meet simulations : understanding skeletal muscle mechanics to address clinical problems(2024) Ateş, Filiz; Röhrle, OliverThis article aims to present some novel experimental approaches and computational methods providing detailed insights into the mechanical behavior of skeletal muscles relevant to clinical problems associated with managing and treating musculoskeletal diseases. The mechanical characterization of skeletal muscles in vivo is crucial for better understanding of, prevention of, or intervention in movement alterations due to exercise, aging, or pathologies related to neuromuscular diseases. To achieve this, we suggest an intraoperative experimental method including direct measurements of human muscle forces supported by computational methodologies. A set of intraoperative experiments indicated the major role of extracellular matrix (ECM) in spastic cerebral palsy. The force data linked to joint function are invaluable and irreplaceable for evaluating individual muscles however, they are not feasible in many situations. Three‐dimensional, continuum‐mechanical models provide a way to predict the exerted muscle forces. To obtain, however, realistic predictions, it is important to investigate the muscle not by itself, but embedded within the respective musculoskeletal system, for example, a 6‐muscle upper arm model, and the ability to obtain non‐invasively, or at least, minimally invasively material parameters for continuum‐mechanical skeletal muscle models, for example, by presently proposed homogenization methodologies. Botulinum toxin administration as a treatment option for spasticity is exemplified by combining experiments with modeling to find out the mechanical outcomes of altered ECM and the controversial effects of the toxin. The potentials and limitations of both experimental and modeling approaches and how they need each other are discussed.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 Coupled simulations and parameter inversion for neural system and electrophysiological muscle models(2024) Homs‐Pons, Carme; Lautenschlager, Robin; Schmid, Laura; Ernst, Jennifer; Göddeke, Dominik; Röhrle, Oliver; Schulte, MiriamThe functioning of the neuromuscular system is an important factor for quality of life. With the aim of restoring neuromuscular function after limb amputation, novel clinical techniques such as the agonist‐antagonist myoneural interface (AMI) are being developed. In this technique, the residual muscles of an agonist‐antagonist pair are (re‐)connected via a tendon in order to restore their mechanical and neural interaction. Due to the complexity of the system, the AMI can substantially profit from in silico analysis, in particular to determine the prestretch of the residual muscles that is applied during the procedure and determines the range of motion of the residual muscle pair. We present our computational approach to facilitate this. We extend a detailed multi‐X model for single muscles to the AMI setup, that is, a two‐muscle‐one‐tendon system. The model considers subcellular processes as well as 3D muscle and tendon mechanics and is prepared for neural process simulation. It is solved on high performance computing systems. We present simulation results that show (i) the performance of our numerical coupling between muscles and tendon and (ii) a qualitatively correct dependence of the range of motion of muscles on their prestretch. Simultaneously, we pursue a Bayesian parameter inference approach to invert for parameters of interest. Our approach is independent of the underlying muscle model and represents a first step toward parameter optimization, for instance, finding the prestretch, to be applied during surgery, that maximizes the resulting range of motion. Since our multi‐X fine‐grained model is computationally expensive, we present inversion results for reduced Hill‐type models. Our numerical results for cases with known ground truth show the convergence and robustness of our approach.Item Open Access Upright posture control in changing gravity conditions(2021) Smirnov, EvgeniiIn order to be able to withstand and to take advantage of external forces and to be able to direct motor actions, living organisms developed ability to sense environmental impacts. For instance, proprioceptors and cutaneous receptors allow vertebrates to take into account, above all, gravitational influences. These receptors participate in planning and correcting posture, locomotion and other movements. In this thesis mechanisms of equilibrium control in changing gravity conditions were studied by means of literature analysis and analysis of data obtained in parabolic flight. This analysis revealed that standing balance in overloading is likely controlled in a manner resembling a single-link inverted pendulum. Such behavior could be beneficial to take advantage of passive body structures and to more actively involve foot receptors in balance regulation in challenging conditions. This adaptation also resembles typical postural responses in balance perturbation tasks. The latter were then studied in more detail. Further literature overview supported the suggestion that plantar foot receptors play an essential role in dynamic stability of upright posture. The obtained conclusions allowed to formulate possible mechanisms of sway and balance control and make suggestions on possible implementation of these mechanisms into the neuromusculoskeletal human model proposed by Walter, Gunther, Haeufle, and Schmitt (2021) in order to make equilibrium control of this model robuster.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)