This repository contains the source code for a future article to be submitted. It investigates the effect of fatigue dynamics on the maximum repetitions of biceps curls.
The repository is made available as accompanying material of the future publication, check out the preprint instead [todo]:
@article{Puchaud2023,
title={},
author={Puchaud, Pierre and Michaud, Benjamin and Begon Mickael},
journal={},
volume={},
number={},
pages={},
year={2023},
}
and includes scripts, models and materials to reproduce figures and results of that work.
Type | Status |
---|---|
License | |
Zenodo | TODO |
In order to run the code, you need to install the following packages from pyomeca:
conda install -c conda-forge biorbd=1.9.9 conda install -c conda-forge bioviz=2.3.0 conda install -c conda-forge bioptim=3.0.1 Extra dependencies are required to run the code.
Predictive simulation of human motion could provide insight into optimal techniques. In repetitive or long-duration tasks, these simulations must predict fatigue-induced adaptation.
However, most studies minimize cost function terms related to actuator activations, assuming it minimizes fatigue.
An additional modeling layer is needed to consider the previous use of muscles to reveal adaptive strategies to the decreased force production capability.
Here, we propose interfacing Xia's three-compartment fatigue dynamics model with rigid-body dynamics. A stabilization invariant was added to Xia’s model. We simulated the maximum repetition of dumbbell biceps curls as an optimal control problem (OCP) using direct multiple shooting.
We explored three cost functions (minimizing minimum torque, fatigue, or both) and two OCP formulations (full-horizon and sliding-horizon approaches).
We found that Xia's model modified with the stabilization invariant (coefficients