Published August 14, 2025 | Version v1
Software Open

Muscle-tendon mechanics resolve the trade-off between energy-efficient and robust locomotion

  • 1. ROR icon Hertie Institute for Clinical Brain Research
  • 2. ROR icon University of Tübingen
  • 3. ROR icon University of Stuttgart
  • 4. ROR icon KU Leuven

Description

This study explores the trade-off between energy-efficient and robust locomotion in muscle-actuated locomotion. We found that muscle-tendon intrinsic mechanics resolve the trade-off between energy-efficient and robust locomotion by solely relying on intrinsic mechanics and a feedforward stimulation strategy that optimized for energy efficiency. 

Corresponding author: Matthew Araz, matthew.araz@uni-tuebingen.de 

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README.md

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Additional details

Related works

Is described by
Data paper: 10.1038/s41598-023-31179-6 (DOI)

Funding

Deutsche Forschungsgemeinschaft
Tunable muscular damping to increase morphological computation in legged locomotion 449912641

Data quality

Accuracy

N/A

Completeness

N/A

Conformity

N/A

Consistency

N/A

Credibility

N/A

Processability

N/A

Relevance

N/A

Timeliness

N/A

Understandability

N/A

Software documentation

Application category
simulation software
Is accessible for free
Yes
Programming language
matlab
Software requirements
Matlab 2023a, Simulink
Software suggestions

Matlab/Simulink

README

Code Running Explanation

The main matlab code for this study is to built to generate the hopping simulations by using a two-segment single-leg hopper model and investigate the trade-off between energy-efficient and robust locomotion at the muscular level for different muscle fiber-tendon length ratios. Results of the current paper can be obtained from the script, which is named "hopping_experiment_with_gamma.m". The following bullet points describe how the code works.

This code initially subtracts the target activity level at touchdown during unperturbed conditions from the data set "Experiment_type=Preflex-Rising_new_stim_limit_slope_cond_1=5_deltaC=0_080597.mat". This data set contains the hopping results with the Preflex-Rising stimulation strategy for FTL. Our optimizer finds the start time for Prefelx-Rising during unperturbed hopping to achieve this target activity level and periodic hopping for a certain FTL ratio.

Later the code, introduces step perturbations to the hopper leg, calculates all necessary data, and saves the data set for the Preflex-Rising condition

Later, the code optimizes the Quasi-Isometric stimulation strategy that achieves the hopping height that Preflex-Rising generates by minimizing the length change of the contractile element. Then, perturbations are introduced to the hopper leg with the Quasi-Isometric stimulation strategy, all necessary data is calculated, and the data set for Quasi-Isometric strategy is also saved in the folder.

Code is arranged in the for loop format. Thus, in one run all the data sets for all FTL conditions can be obtained. Note that data sets are saved as gamma_cond=1,2 or 3. Condition order goes from small to higher gamma values.

For obtaining the figures presented in the manscript and the Supplementary Material, the 'plotting_script_gamma.m' script must be run. This code generates all the figures for all FTL condition in single run and saves them in the folder.