Christian Attenberger

Advisors
Marlies Nitschke (M.Sc.), Prof. Dr. Anne Koelewijn, Prof. Dr. Björn Eskofier

Duration
09/2020 – 04/2021

Abstract

Computer models, like finite element models, are increasingly used for virtual design of for example footwear. However, these models do not include the musculoskeletal or behavioural mechanisms of the human. Hence, building prototypes and testing them in experimental studies is still a common but time- and cost-intensive step during the design process. During the last years, our group researched simulations of musculoskeletal models to predict performance differences based on changes in footwear. The effect of added limb mass on energy cost was simulated using one musculoskeletal model and data of one subject [1]. As extension, multiple musculoskeletal models and data of multiple subjects were used to simulate the effect of BOOST versus EVA midsole material. [2]. Due to the virtual study design, the results were insensitive to model parameters and representative for a population of runners. A public available data set with various running styles [3] was used to avoid data recording. The relative differences of metabolic cost were in agreement with independent human oxygen uptake experiments for both simulation studies [1, 2], validating the simulated predictions. Later, the virtual study design was used to investigate the effect of softness and energy loss of midsole materials, of sole geometries and of a newly developed foam.

However, so far, it was only shown that a trend can be predicted for a population, but it was not yet investigated if individual responses can be predicted. This is of special interest for athlete tailored footwear or to give individual recommendations to customers since individual running patterns are hardly affected by small changes in footwear according to the preferred movement
path paradigm [4].

The purpose of this work is to verify the accuracy of the predictive simulation for virtual footwear design by analysing individual running patterns. This will be done exemplarily for a newly developed shoe prototype.

 
References:

1. Van den Bogert, A. J., Hupperets, M., Schlarb, H., and Krabbe, B. (2012). Predictive musculoskeletal simulation using optimal control: Effects of added limb mass on energy cost and kinematics of walking and running. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 226(2), 123-133.
2. Dorschky, E., Krüger, D., Kurfess, N., Schlarb, H., Wartzack, S., Eskofier, B. M., and Van den Bogert, A. J. (2019). Optimal control simulation predicts effects of midsole materials on energy cost of running. Computer Methods in Biomechanics and Biomedical Engineering, 22, 869-879.
3. Fukuchi, R. K., Fukuchi, C. A., and Duarte, M. (2017). A public dataset of running biomechanics and the effects of running speed on lower extremity kinematics and kinetics. PeerJ, 5, e3298.
4. Nigg, B. M., Vienneau, J., Smith, A. C., Trudeau, M. B., Mohr, M., and Nigg, S. R. (2017). The Preferred Movement Path Paradigm: Influence of Running Shoes on Joint Movement. Med & Sci Sports & Exercise, 49(8), 1641-1648.