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Full-Text Articles in Mechanical Engineering
Antagonistic Co-Contraction Can Minimize Muscular Effort In Systems With Uncertainty, Anne D. Koelewijn, Antonie J. Van Den Bogert
Antagonistic Co-Contraction Can Minimize Muscular Effort In Systems With Uncertainty, Anne D. Koelewijn, Antonie J. Van Den Bogert
Mechanical Engineering Faculty Publications
Muscular co-contraction of antagonistic muscle pairs is often observed in human movement, but it is considered inefficient and it can currently not be predicted in
simulations where muscular effort or metabolic energy are minimized. Here, we investigated the relationship between minimizing effort and muscular co-contraction
in systems with random uncertainty to see if muscular co-contraction can minimize effort in such system. We also investigated the effect of time delay in the muscle, by varying the time delay in the neural control as well as the activation time constant.We solved optimal control problems for a one-degree-of-freedom pendulum actuated by two identical …
Cnn-Based Estimation Of Sagittal Plane Walking And Running Biomechanics From Measured And Simulated Inertial Sensor Data, Eva Dorschky, Marlies Nitschke, Christine F. Martindale, Antonie J. Van Den Bogert, Anne D. Koelewijn, Bjoern M. Eskofier
Cnn-Based Estimation Of Sagittal Plane Walking And Running Biomechanics From Measured And Simulated Inertial Sensor Data, Eva Dorschky, Marlies Nitschke, Christine F. Martindale, Antonie J. Van Den Bogert, Anne D. Koelewijn, Bjoern M. Eskofier
Mechanical Engineering Faculty Publications
Machine learning is a promising approach to evaluate human movement based on wearable sensor data. A representative dataset for training data-driven models is crucial to ensure that the model generalizes well to unseen data. However, the acquisition of sufficient data is time-consuming and often infeasible. We present a method to create realistic inertial sensor data with corresponding biomechanical variables by 2D walking and running simulations. We augmented a measured inertial sensor dataset with simulated data for the training of convolutional neural networks to estimate sagittal plane joint angles, joint moments, and ground reaction forces (GRFs) of walking and running. When …
Opty: Software For Trajectory Optimization And Parameter Identification Using Direct Collocation, Jason K. Moore, Antonie J. Van Den Bogert
Opty: Software For Trajectory Optimization And Parameter Identification Using Direct Collocation, Jason K. Moore, Antonie J. Van Den Bogert
Mechanical Engineering Faculty Publications
opty is a tool for describing and solving trajectory optimization and parameter identification problems based on symbolic descriptions of ordinary differential equations and differential algebraic equations that describe a dynamical system. The motivation for its development resides in the need to solve optimal control problems of biomechanical systems. The target audience is engineers and scientists interested in solving nonlinear optimal control and parameter identification problems with minimal computational overhead.
Predictive Musculoskeletal Simulation Using Optimal Control: Effects Of Added Limb Mass On Energy Cost And Kinematics Of Walking And Running, Antonie J. Van Den Bogert, Maarten Hupperets, Heiko Schlarb, Berthold Krabbe
Predictive Musculoskeletal Simulation Using Optimal Control: Effects Of Added Limb Mass On Energy Cost And Kinematics Of Walking And Running, Antonie J. Van Den Bogert, Maarten Hupperets, Heiko Schlarb, Berthold Krabbe
Mechanical Engineering Faculty Publications
When designing sports equipment, it is often desirable to predict how certain design parameters will affect human performance. In many instances, this requires a consideration of human musculoskeletal mechanics and adaptive neuromuscular control. Current computational methods do not represent these mechanisms, and design optimization typically requires several iterations of prototyping and human testing. This paper introduces a computational method based on musculoskeletal modeling and optimal control, which has the capability to predict the effect of mechanical equipment properties on human performance. The underlying assumption is that users will adapt their neuromuscular control according to an optimality principle, which balances task …
Predictive Simulation Of Gait At Low Gravity Reveals Skipping As The Preferred Locomotion Strategy, Marko Ackermann, Antonie J. Van Den Bogert
Predictive Simulation Of Gait At Low Gravity Reveals Skipping As The Preferred Locomotion Strategy, Marko Ackermann, Antonie J. Van Den Bogert
Mechanical Engineering Faculty Publications
The investigation of gait strategies at low gravity environments gained momentum recently as manned missions to the Moon and to Mars are reconsidered. Although reports by astronauts of the Apollo missions indicate alternative gait strategies might be favored on the Moon, computational simulations and experimental investigations have been almost exclusively limited to the study of either walking or running, the locomotion modes preferred under Earth's gravity. In order to investigate the gait strategies likely to be favored at low gravity a series of predictive, computational simulations of gait are performed using a physiological model of the musculoskeletal system, without assuming …
Implicit Methods For Efficient Musculoskeletal Simulation And Optimal Control, Antonie J. Van Den Bogert, Dimitra Blana, Dieter Heinrich
Implicit Methods For Efficient Musculoskeletal Simulation And Optimal Control, Antonie J. Van Den Bogert, Dimitra Blana, Dieter Heinrich
Mechanical Engineering Faculty Publications
The ordinary differential equations for musculoskeletal dynamics are often numerically stiff and highly nonlinear. Consequently, simulations require small time steps, and optimal control problems are slow to solve and have poor convergence. In this paper, we present an implicit formulation of musculoskeletal dynamics, which leads to new numerical methods for simulation and optimal control, with the expectation that we can mitigate some of these problems. A first order Rosenbrock method was developed for solving forward dynamic problems using the implicit formulation. It was used to perform real-time dynamic simulation of a complex shoulder arm system with extreme dynamic stiffness. Simulations …
Optimality Principles For Model-Based Prediction Of Human Gait, Marko Ackermann, Antonie J. Van Den Bogert
Optimality Principles For Model-Based Prediction Of Human Gait, Marko Ackermann, Antonie J. Van Den Bogert
Mechanical Engineering Faculty Publications
Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient's gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like …