ANALYSIS OF SKELETAL MOTION KINEMATICS FOR A KNEE MOVEMENT CYCLE

This study estimated the skeletal motion for a knee motion cycle. The surface markers on the thigh and the shank showed the computed displacement during in vivo motion analysis. This error was minimized using optimization procedure. The displacement was generally greater on the thigh than the shank. The minimization of error produced by this procedure was more successful on the thigh than the shank. The purpose of this study was to require high value motion data. These results provide the basis to calculate the instantaneous knee axis of rotation in a follow up stud

ESTIMATION OF THE MOVING JOINT AXIS IN THE KNEE JOINT BY MOTION ANALYSIS DATA

It is essential to use individually parameterized models for the knee joint as well as for the patellofemoral joint while analyzing the correlations between external and internal loads and the efficiency of specific training exercises for the lower extremities. A new approach to estimate the moving joint axis within the knee joint using motion analysis data was evaluated. The results of this single case study show that this approach might offer a possibility to parameterize an individualized knee joint model without using MRI scans

ACCURACY OF CALCULATED KNEE JOINT MOVEMENTS DEPENDING ON MARKER SETS AND LEG POSITION

This study investigated the influence of different marker sets and different leg positions on time histories of skeletal kinematics of the lower limb. Surface markers were attached to the thigh and the shank to reproduce their kinematics during a knee movement cycle. Certain selections of posture and marker sets minimised the expected measurement errors without further optimisation procedures. However, the results showed an approximation to skeletal movement, only. The results lead to recommendations for the use of skin based marker systems

Effect of the economic outturn on the cost of debt of an industrial enterprise

The cost of debt is referred to as the key factor determining profitability. It is a decisive factor in decision making of the management, especially in strategy development. The purpose of this paper is to establish the relationship between the volume of debt and the economic outturn of industrial enterprises. Using artificial neural networks, the relationship between interest costs and three profit categories is examined. Data of 5622 Czech processing enterprises in the years 2015-2017 are used. Multilayer perceptron neural networks and neural networks of basic radial functions are used for processing. A total of 10,000 neural structures are generated for each cost-interest relationship and the corresponding profit, of which 5 are retained, showing the best results. The results indicate that in all cases of profit there is no dependence between the interest and the amount of profit generated. Profiting companies do not get debt cheaper than other businesses

Levels of explicability for medical artificial intelligence: what do we normatively need and what can we technically reach?

Definition of the problem The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. Which levels of explicability are needed to obtain informed consent when utilizing medical AI? Arguments We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example. Conclusion We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements

Ein Open Source-basiertes Learning Managementsystem für Volksbanken

<br