Luuk Vos
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Luuk Vos has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Arthur J. ‘Knoek’ van Soest
Department of Human Movement Sciences, Vrije Universiteit Amsterdam – Amsterdam, The Netherlands
1 shared publication - Dinant A. Kistemaker
Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, The Netherlands
1 shared publication - Guido Weide
Amsterdam Movement Sciences
1 shared publication - Jos J. de Koning
Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
1 shared publication - Koen K. Lemaire
Department of Human Movement Sciences, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, The Netherlands
1 shared publication Renske N. Vergeer
Amsterdam Movement Sciences
1 shared publication
Latest Publications
- Open Access
Coupling Mechanical and Metabolic Behavior in Huxley-type Muscle Models
Accurate prediction of both mechanical and metabolic behavior of muscle remains an important challenge in biomechanics. The typically employed Hill-type muscle models have shown limited success in this regard. Therefore, Huxley-type models, in which mechanical and metabolic behavior is linked through cross-bridge cycling, have gained renewed attention. Previous studies fitted the cross-bridge cycling rate parameter values of such models on mechanical behavior only. It seems reasonable to assume that accurate predictions of mechanical behavior in a Huxley-type model will also result in accurate predictions of metabolic behavior as both depend on the cross-bridge dynamics. Here, we show that this assumption does not hold. We simulated previously collected mechanical and metabolic data from an experiment where participants performed either isometric or dynamic knee extensions in the gravitational field. We modeled this experiment with a musculoskeletal model consisting of two segments driven by one Huxley-type muscle model. We obtained 10 sets of cross-bridge rate parameter values by systematically varying the value of one of the rate parameters and optimizing the values of the remaining rate parameters with respect to the mechanical behavior. We then compared the predicted mechanical and metabolic behavior between the 10 sets. The predicted mechanical behavior was similar for all 10 sets. However, the accuracy of the predicted metabolic behavior differed substantially between the 10 sets. Our findings illustrate that different sets of cross-bridge rate parameter values may lead to similar mechanical behavior. We conclude that this should be exploited to obtain accurate predictions of mechanical and metabolic behavior simultaneously in Huxley-type muscle models.
Open AccessCoupling Mechanical and Metabolic Behavior in Huxley-type Muscle Models
Accurate prediction of both mechanical and metabolic behavior of muscle remains an important challenge in biomechanics. The typically employed Hill-type muscle models have shown limited success in this regard. Therefore, Huxley-type models, in which mechanical and metabolic behavior is linked through cross-bridge cycling, have gained renewed attention. Previous studies fitted the cross-bridge cycling rate parameter values of such models on mechanical behavior only. It seems reasonable to assume that accurate predictions of mechanical behavior in a Huxley-type model will also result in accurate predictions of metabolic behavior as both depend on the cross-bridge dynamics. Here, we show that this assumption does not hold. We simulated previously collected mechanical and metabolic data from an experiment where participants performed either isometric or dynamic knee extensions in the gravitational field. We modeled this experiment with a musculoskeletal model consisting of two segments driven by one Huxley-type muscle model. We obtained 10 sets of cross-bridge rate parameter values by systematically varying the value of one of the rate parameters and optimizing the values of the remaining rate parameters with respect to the mechanical behavior. We then compared the predicted mechanical and metabolic behavior between the 10 sets. The predicted mechanical behavior was similar for all 10 sets. However, the accuracy of the predicted metabolic behavior differed substantially between the 10 sets. Our findings illustrate that different sets of cross-bridge rate parameter values may lead to similar mechanical behavior. We conclude that this should be exploited to obtain accurate predictions of mechanical and metabolic behavior simultaneously in Huxley-type muscle models.
- Open Access
- 10 October 2025
- Published by Taylor & Francis in Applied Artificial Intelligence
- Vol. 39 (1)
Predicting Cycling Performance Before and After Training: Insights From Machine Learning Using Small Samples
- 10 October 2025
- Published by Taylor & Francis in Applied Artificial Intelligence
- Vol. 39 (1)
Open AccessPredicting Cycling Performance Before and After Training: Insights From Machine Learning Using Small Samples