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Full-Text Articles in Engineering
Efficient Trajectory Optimization For Curved Running Using A 3d Musculoskeletal Model With Implicit Dynamics, Marlies Nitschke, Eva Dorschky, Dieter Heinrich, Heiko Schlarb, Bjoern M. Eskofier, Anne D. Koelewijn, Antonie J. Van Den Bogert
Efficient Trajectory Optimization For Curved Running Using A 3d Musculoskeletal Model With Implicit Dynamics, Marlies Nitschke, Eva Dorschky, Dieter Heinrich, Heiko Schlarb, Bjoern M. Eskofier, Anne D. Koelewijn, Antonie J. Van Den Bogert
Mechanical Engineering Faculty Publications
Trajectory optimization with musculoskeletal models can be used to reconstruct measured movements and to predict changes in movements in response to environmental changes. It enables an exhaustive analysis of joint angles, joint moments, ground reaction forces, and muscle forces, among others. However, its application is still limited to simplified problems in two dimensional space or straight motions. The simulation of movements with directional changes, e.g. curved running, requires detailed three dimensional models which lead to a high-dimensional solution space. We
extended a full-body three dimensional musculoskeletal model to be specialized for running with directional changes. Model dynamics were implemented implicitly …
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 …