Musculoskeletal Simulation in Sports and Medicine
Speaker: Bjoern M Eskofier – Erlangen, GermanyTopic(s): Applied Computing
Abstract
Musculoskeletal Simulation is applied to analyze and understand human movement e.g., for medical assessments, performance analysis, the study of human-product interaction. The objective is either to reconstruct human movement from measurement data (tracking problem) or to predict human responses (prediction problem). Both are presented in the talk: (I) the reconstruction of human movement from inertial sensor data and (II) the prediction of human responses in sports product design.
(I) Inertial sensor systems provide the possibility of movement analysis in everyday life. One major challenge is to achieve a high-quality movement analysis based on noisy and sparse sensor measurements. Moreover, inertial sensors can only quantify human joint kinematics and are not able to measure joint kinetics as performed in movement analysis laboratories. Existing systems are based on an integration of the inertial sensor data for estimating human poses. This error-prone integration can be avoided using a computer simulation of a biomechanical model that tracks the measured sensor signals. Furthermore, such a model can give insight into joint kinetics, muscle control and other gait-related parameters such as stride length, stride time and ground-reaction force in gait.
(II) Running shoes are designed to improve running economy. But, how to derive optimal design parameters? The conventional process of prototyping and testing is often time-consuming, too expensive or even not realizable. Musculoskeletal simulation using optimal control, however, avoids human testing and allows to predict how human motion changes due to mechanical factors. Modeling the contact mechanics depending on shoe properties allows to predict the influence of design parameters on running mechanics and performance.
About this Lecture
Number of Slides: 20 - 40Duration: 20 - 60 minutes
Languages Available: English, German
Last Updated:
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