Daniel Nolte

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Segment-based musculoskeletal models allow the prediction of muscle, ligament, and joint forces without making assumptions regarding joint degrees-of-freedom (DOF). The dataset published for the "Grand Challenge Competition to Predict in vivo Knee Loads" provides directly measured tibiofemoral contact forces for activities of daily living (ADL). For the(More)
Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this(More)
For many medical applications, it's challenging to access large datasets, which are often hosted across different domains on heterogeneous infrastructures. Homogenizing the infrastructure to simplify data access is unrealistic; therefore, it's important to develop distributed storage that doesn't introduce added complexity. Here, a solution is investigated(More)
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