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Effective training requires high muscle forces potentially leading to training-induced injuries. Thus, continuous monitoring and controlling of the loadings applied to the musculoskeletal system along the motion trajectory is required. In this paper, a norm-optimal iterative learning control algorithm for the robot-assisted training is developed. The algorithm aims at minimizing the external knee joint moment, which is commonly used to quantify the loading of the medial compartment. To estimate the external knee joint moment, a musculoskeletal lower extremity model is implemented in OpenSim and coupled with a model of an industrial robot and a force plate mounted at its end-effector. The algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee. The results show that the algorithm is able to minimize the external knee joint moment in all three cases and converges after less than seven iterations.
In: Net-distributed Co-operation : Xth International Conference on Computing in Civil and Building Engineering, Weimar, June 02 - 04, 2004 ; proceedings / [ed. by Karl Beuke ...] . - Weimar: Bauhaus-Univ. Weimar 2004. - 1. Aufl. . Seite 1-14 ISBN 3-86068-213-X International Conference on Computing in Civil and Building Engineering <10, 2004, Weimar> Summary In our project, we develop new tools for the conceptual design phase. During conceptual design, the coarse functionality and organization of a building is more important than a detailed worked out construction. We identify two roles, first the knowledge engineer who is responsible for knowledge definition and maintenance; second the architect who elaborates the conceptual de-sign. The tool for the knowledge engineer is based on graph technology, it is specified using PROGRES and the UPGRADE framework. The tools for the architect are integrated to the in-dustrial CAD tool ArchiCAD. Consistency between knowledge and conceptual design is en-sured by the constraint checker, another extension to ArchiCAD.