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Does Difficulty even Matter? Investigating Difficulty Adjustment and Practice Behavior in an Open-Ended Learning Task
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A new musculoskeletal AnyBody™ detailed hand model
Get PDFMusculoskeletal research questions regarding the prevention or rehabilitation of the hand can be addressed using inverse dynamics simulations when experiments are not possible. To date, no complete human hand model implemented in a holistic human body model has been fully developed. The aim of this work was to develop, implement, and validate a fully detailed hand model using the AnyBody Modelling System (AMS) (AnyBody, Aalborg, Denmark). To achieve this, a consistent multiple cadaver dataset, including all extrinsic and intrinsic muscles, served as a basis. Various obstacle methods were implemented to obtain with the correct alignment of the muscle paths together with the full range of motion of the fingers. These included tori, cylinders, and spherical ellipsoids. The origin points of the lumbrical muscles within the tendon of the flexor digitorum profundus added a unique feature to the model. Furthermore, the possibility of an entire patient-specific scaling based on the hand length and width were implemented in the model. For model validation, experimental datasets from the literature were used, which included the comparison of numerically calculated moment arms of the wrist, thumb, and index finger muscles. In general, the results displayed good comparability of the model and experimental data. However, the extrinsic muscles showed higher accordance than the intrinsic ones. Nevertheless, the results showed, that the proposed developed inverse dynamics hand model offers opportunities in a broad field of applications, where the muscles and joint forces of the forearm play a crucial role
Do you Feel Safe? How Weather Conditions and Time of Day Affect the Feeling of Safety in Passenger Drones
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Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation
No full textApplying Concept-Based Models for Enhanced Safety Argumentation – Summary
Get PDFIn this extended abstract we summarize our work on using Concept Bottleneck Models (CBMs) for an enhanced safety argumentation of vision-based Machine Learning (ML) perception components in safety critical systems. This paper has been published at the International Symposium on Software Reliability Engineering (ISRRE’24
Is Maximum Entropy Deep Inverse Reinforcement Learning Suitable for Pedestrian Path Prediction?
No full text3D printing with steel
Get PDFAutomated production is finding its way into the fabrication of structural steel. One robot holds attachments (stiffeners, end plates, etc.) on a steel beam or column and another robot produces weld seams. However, welding robots can also be used for Additive Manufacturing (Wire and Arc Additive Manufacturing, WAAM). The wire electrode serves as a printing material. The Institute of Steel Construction and Materials Mechanics in Darmstadt is investigating how typical connecting elements for steel structures can be printed directly on steel beams using Additive Manufacturing with arc welding and robots. Furthermore, structural elements such as nodes for space frames can be printed and even complete structures, e.g. columns and a little bridge, have already been manufactured additively. The main focus is on determining suitable welding and process parameters. In addition, topology optimization is necessary in order to achieve good structures using a small amount of material. This is possible due to the free design prospects of WAAM, which opens up new design and production strategies