A Simple Method for Measuring Stiffness during Running
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- Jean-Benoît Morin
- 1University of Saint-Etienne
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- Georges Dalleau
- 2University of La Réunion
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- Heikki Kyröläinen
- 3University of Jyväskylä
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- Thibault Jeannin
- 1University of Saint-Etienne
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- Alain Belli
- 1University of Saint-Etienne
Description
<jats:p>The spring-mass model, representing a runner as a point mass supported by a single linear leg spring, has been a widely used concept in studies on running and bouncing mechanics. However, the measurement of leg and vertical stiffness has previously required force platforms and high-speed kinematic measurement systems that are costly and difficult to handle in field conditions. We propose a new “sine-wave” method for measuring stiffness during running. Based on the modeling of the force-time curve by a sine function, this method allows leg and vertical stiffness to be estimated from just a few simple mechanical parameters: body mass, forward velocity, leg length, flight time, and contact time. We compared this method to force-platform-derived stiffness measurements for treadmill dynamometer and overground running conditions, at velocities ranging from 3.33 m·s<jats:sup>–1</jats:sup>to maximal running velocity in both recreational and highly trained runners. Stiffness values calculated with the proposed method ranged from 0.67% to 6.93% less than the force platform method, and thus were judged to be acceptable. Furthermore, significant linear regressions (<jats:italic>p</jats:italic>< 0.01) close to the identity line were obtained between force platform and sine-wave model values of stiffness. Given the limits inherent in the use of the spring-mass model, it was concluded that this sine-wave method allows leg and stiffness estimates in running on the basis of a few mechanical parameters, and could be useful in further field measurements.</jats:p>
Journal
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- Journal of Applied Biomechanics
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Journal of Applied Biomechanics 21 (2), 167-180, 2005-05
Human Kinetics
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Details 詳細情報について
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- CRID
- 1362262945195588608
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- ISSN
- 15432688
- 10658483
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- Data Source
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- Crossref