THEORETICAL MODEL VALIDATION OF MUSCLE FORCES DURING EXTREME MOVEMENTS

Introduction: The validation of the involved muscular forces for a computer model of the human body, which allows simulation of internal forces in patients, was achieved by inverse-dynamic analysis. Methods: Beginning with an extreme vertical jump, joint moments were extracted from high-speed film data and eventually subdivided into muscular forces. The muscle groups responsible for movements were determined by electromyography. A squat jump with both legs and maximum strength was filmed in the sagittal plane with a frequency of 200 Hz. Moments in the hip, knee and ankle joints were determined from the film data. Using surface electrodes of a Neuraxon Myosoft 2008 system and an amplifier system from Multichannel Systems, the muscle groups responsible for movements were electromyographically determined. The muscle insertions and muscle paths were extracted from MRI pictures of patients. Results: With this information joint moments can be subdivided into single muscle forces. Depending on the jump demands, the muscle groups responsible for movements can be divided and analyzed in six extensor groups. Conclusion: Inverse-dynamic muscle force analysis is a basis which can be expanded for the validation of complex movements under extreme internal loads in patients

FORWARD DYNAMICS FOR THE EVALUATION OF PRACTICAL PROBLEMS IN SPORTS

INTRODUCTION: In athletic movements there are often situations where one cannot rate varying executions, because the effects of single actions are unknown. At a tennis stroke for example, the movement of the ball after hitting is well visible as an effect of the action. However, the conditions at hitting the ball and the actions that lead to the torque of hitting are not reliably visible. Their interpretation is only subjective. Nevertheless, the trainer and the player have to give statements of the muscular activity like "hold the racket loosely or firmly "or"relax or stiffen your wrist." This paper focuses on a controversial problem: the use of the wrist in tennis. Some favor a firm wrist, others an actively moving wrist. The group which favors the active wrist based their idea on the higher velocities of the racket head. For this idea biomechanical considerations are only based on kinematic data and on analysis in muscular physiology (see KLEINÖDER 1997, ELLIOT 1991, HUIJING 1994, KOMI 1994) and not on kinetic analysis. With this work we try to fill these gaps with computer simulation. In a similar way we worked on a problem in gymnastics: the increase of swings on the horizontal bar, which is necessary for all swing elements. Little work has been done in this area (see BAUER 1976, BÖHM1997 and WIEMANN 1993). Nevertheless, the research that allows a development of a general theory of the swing increase is lacking (except for the efforts of WIEMANN). The goal of this paper is to show that computer simulation can be a first step towards the development of such a theory

Big Data and Discrete Optimization for Electric Urban Bus Operations

The electrification of urban bus fleets is a challenging task, especially for smaller public transport operators. The main challenge lies in the uncertainty about many technical aspects, like range of vehicles under different circumstances or charging times, that are new for the operators. The purpose of this research is to introduce an approach to solve this problem by incorporating all available data from an existing bus fleet and finding an optimal solution with discrete mathematical optimization. Extensive data logging in the project enabled us to leverage tracking data from the whole bus network including trajectories, powertrain data, and operational data. This enabled us to validate assumptions about the energy demand, waiting times, and different traffic situations during the day. To get better insights into the requirements of an urban bus fleet, we simulated the potential electric buses in detail and extracted other necessary data like actual dwell times. Based on the simulation results and processed data, we implemented a linear programming model to search for a cost-optimal configuration of vehicles and charging infrastructure. We tested the framework with a scenario in which we analyzed the solutions with different numbers of diesel buses in the fleet. The application of our algorithm shows that it can produce optimal results in a short amount of time, for a medium-sized city in Germany. We also demonstrate that the flexible and constraint-based formulation of this approach allows it to be incorporated in the planning process of most public transport operators

A functional approach to movement analysis and error identification in sports and physical education

In a hypothesis-and-theory paper, a functional approach to movement analysis in sports is introduced. In this approach, contrary to classical concepts, it is not anymore the ideal movement of elite athletes that is taken as a template for the movements produced by learners. Instead, movements are understood as the means to solve given tasks that in turn, are defined by to-be-achieved task goals. A functional analysis comprises the steps of (1) recognising constraints that define the functional structure, (2) identifying sub-actions that subserve the achievement of structure-dependent goals, (3) explicating modalities as specifics of the movement execution, and (4) assigning functions to actions, sub-actions and modalities. Regarding motor-control theory, a functional approach can be linked to a dynamical-system framework of behavioural shaping, to cognitive models of modular effect-related motor control as well as to explicit concepts of goal setting and goal achievement. Finally, it is shown that a functional approach is of particular help for sports practice in the context of structuring part practice, recognising functionally equivalent task solutions, finding innovative technique alternatives, distinguishing errors from style, and identifying root causes of movement errors

Optimising Manufacturing Process with Bayesian Structure Learning and Knowledge Graphs

In manufacturing industry, product failure is costly, as it results in financial and time losses. Understanding the causes of product failure is critical for reducing the occurrence of failure and optimising the manufacturing process. As a result, a number of studies utilising data-driven approaches such as machine learning have been conducted to reduce the occurrence of this failure and to improve the manufacturing process. While these data-driven approaches enable pattern recognition, they lack the advantages associated with knowledge-driven approaches, such as knowledge representation and deductive reasoning. Similarly, knowledge-driven approaches lack the pattern-learning capabilities inherent in data-driven approaches such as machine learning. Therefore, in this paper, leveraging the advantages of both data-driven and knowledge-driven approaches, we present a strategy with a prototype implementation to reduce manufacturing product failure. The proposed strategy combines a data-driven technique, Bayesian structural learning, with a knowledge-based technique, knowledge graphs