ArticlePloS one2022
A direct collocation framework for optimal control simulation of pedaling using OpenSim.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 30 citations in OpenAlex.
- From simulation to stimulation: a predictive synergy-based control strategy for functional electrical stimulation cycling in a person with spinal cord injury.Journal of neuroengineering and rehabilitation · 2026Article
- Visualizing joint force-velocity properties in musculoskeletal models.Royal Society open science · 2025Article
- A physiologically inspired hybrid CPG/Reflex controller for cycling simulations that generalizes to walking.PLoS computational biology · 2025Article
- Biomechanical Analysis of Human Gait When Changing Velocity and Carried Loads: Simulation Study with OpenSim.Biology · 2024Article
- Higher coactivations of lower limb muscles increase stability during walking on slippery ground in forward dynamics musculoskeletal simulation.Scientific reports · 2023Article
- Muscle-driven simulations and experimental data of cycling.Scientific reports · 2023Article
- Model-based estimation of muscle and ACL forces during turning maneuvers in alpine skiing.Scientific reports · 2023Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The direct collocation (DC) method has shown low computational costs in solving optimization problems in human movements, but it has rarely been used for solving optimal control pedaling problems. Thus, the aim of this study was to develop a DC framework for optimal control simulation of human pedaling within the OpenSim modeling environment. A planar bicycle-rider model was developed in OpenSim. The DC method was formulated in MATLAB to solve an optimal control pedaling problem using a data tracking approach. Using the developed DC framework, the optimal control pedaling problem was successfully solved in 24 minutes to ten hours with different objective function weightings and number of nodes from two different initial conditions. The optimal solutions for equal objective function weightings were successful in terms of tracking, with the model simulated pedal angles and pedal forces within ±1 standard deviation of the experimental data. With these weightings, muscle tendon unit (MTU) excitation patterns generally matched with burst timings and shapes observed in the experimental EMG data. Tracking quality and MTU excitation patterns were changed little by selection of node density above 31, and the optimal solution quality was not affected by initial guess used. The proposed DC framework could easily be turned into a predictive simulation with other objective functions such as fastest pedaling rate. This flexible and computationally efficient framework should facilitate the use of optimal control methods to study the biomechanics, energetics, and control of human pedaling.
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Registered trials
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