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Techn. Fakultät Willkommen am Institut für Informatik FAU-Logo

Eva Dorschky M. Sc.

Researcher in the Digital Sports group at the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg

Predictive Simulation of Uphill and Downhill Running
  • Predicting human responses to environmental changes is required for biomechanical analysis and sports product design. If case studies, environmental conditions or prototypes cannot be realized, modeling and simulation can be used instead. The aim of this work was to evaluate a method of predictive musculoskeletal simulation (van den Bogert et al., 2012) for uphill and downhill running. The predicted energy costs for running at different slopes were compared to literature (Minetti et al., 2002). 

     

    Articles in Conference Proceedings
    Dorschky, Eva; van den Bogert, Antonie J.; Schlarb, Heiko; Eskofier, Björn
    Predictive Musculoskeletal Simulation of Uphill and Downhill Running
    Book of Abstracts of the 20th Annual Congress of the European College of Sport Science (20th Annual Congress of the European College of Sport Science), Malmö - Schweden, 24.06.2015 - 27.06.2015, pp. 126, 2015, ISBN 978-91-7104-567-6 (BiBTeX, Who cited this?)
Predicting Human Responses
  • The aim of this project is to predict the effect of sports equipment and rehabilitation devices on human movement and performance. It is difficult, costly, unethical, or even impossible to answer question through human testing. Therefore, we develop methods for biomechanical modeling and simulation for predicting human responses. The investigation of new methods is necessary to provide a more realistic simulation while considering model complexity and computing time. We enhance current methods which already allow a 2D simulation. This project is in cooperation with adidas AG and the Human Motion & Control Lab at the Cleveland State University. The framework should be integrated and provided to the Opens external link in new windowOpenSim community.

     

Early Event Detection
  • A considerable number of wearable system applications necessitate early event detection (EED). EED is defined as the detection of an event with as much lead time as possible. Applications include physiological (e.g., epileptic seizure or heart stroke) or biomechanical (e.g., fall movement or sports event) monitoring systems. EED for wearable systems is under-investigated in literature. Therefore, we introduce a novel EED framework for wearable systems based on hybrid Hidden Markov Models. Our study specifically targets EED based on inertial measurement unit (IMU) signals in sports. We investigate the early detection of high intensive soccer kicks, with the possible pre-kick adaptation of a soccer shoe before the shoe-ball impact in mind. We conducted a study with ten subjects and recorded 226 kicks using a custom IMU placed in a soccer shoe cavity. We evaluated our framework in terms of EED accuracy and EED latency. In conclusion, our framework delivers the required accuracy and lead times for EED of soccer kicks and can be straightforwardly adapted to other wearable system applications that necessitate EED. 

     

    Articles in Conference Proceedings
    A Framework for Early Event Detection for Wearable Systems
    Proceedings of the 2015 ACM International Symposium on Wearable Computers (The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing), Osaka, Japan, 07.09.2015 - 11.09.2015, pp. 109-112, 2015, ISBN 978-1-4503-3578-2 (BiBTeX, Who cited this?)