Comparison of direct collocation optimal control to trajectory optimization for parameter identification of an ellipsoidal foot–ground contact model

Ezati, Mahdokht; Brown, Peter A.; Ghannadi, Borna; McPhee, John

doi:10.1007/s11044-020-09731-3

Cited by 13 publications

(3 citation statements)

References 41 publications

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“…This can be explained by the model attempting to position the foot closer to the ground to allow the foot-ground contact model, which is driven by the kinematics, to generate the matching GRF. Future work exploring the geometry of the foot-ground contact model, similar to Ezati et al (2020) , is possibly needed to improve the timing of GRF production.…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach

Haralabidis

Serrancolí

Colyer

et al. 2021

PeerJ

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Biomechanical simulation and modelling approaches have the possibility to make a meaningful impact within applied sports settings, such as sprinting. However, for this to be realised, such approaches must first undergo a thorough quantitative evaluation against experimental data. We developed a musculoskeletal modelling and simulation framework for sprinting, with the objective to evaluate its ability to reproduce experimental kinematics and kinetics data for different sprinting phases. This was achieved by performing a series of data-tracking calibration (individual and simultaneous) and validation simulations, that also featured the generation of dynamically consistent simulated outputs and the determination of foot-ground contact model parameters. The simulated values from the calibration simulations were found to be in close agreement with the corresponding experimental data, particularly for the kinematics (average root mean squared differences (RMSDs) less than 1.0° and 0.2 cm for the rotational and translational kinematics, respectively) and ground reaction force (highest average percentage RMSD of 8.1%). Minimal differences in tracking performance were observed when concurrently determining the foot-ground contact model parameters from each of the individual or simultaneous calibration simulations. The validation simulation yielded results that were comparable (RMSDs less than 1.0° and 0.3 cm for the rotational and translational kinematics, respectively) to those obtained from the calibration simulations. This study demonstrated the suitability of the proposed framework for performing future predictive simulations of sprinting, and gives confidence in its use to assess the cause-effect relationships of technique modification in relation to performance. Furthermore, this is the first study to provide dynamically consistent three-dimensional muscle-driven simulations of sprinting across different phases.

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Section: Discussionmentioning

confidence: 99%

Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach

Haralabidis

Serrancolí

Colyer

et al. 2021

PeerJ

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“…It uses elastic foundation theory to represent contact loads distributed over a surface, instead of acting at discrete points. Some initial work using volumetric contact in foot-ground contact has been done using spheres [61] and ellipsoids [62,63], and it has also been used to model impacts in sports applications [32].…”

Section: Normal Force Modelsmentioning

confidence: 99%

Predictive multibody dynamic simulation of human neuromusculoskeletal systems: a review

et al. 2022

Self Cite

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Over the past decade, there has been a rapid increase in applications of multibody system dynamics to the predictive simulation of human movement. Using predictive "what-if" human dynamic simulations that do not rely on experimental testing or prototypes, new medical interventions and devices can be developed more quickly, cheaply, and safely. In this paper, we provide a comprehensive review of research into the predictive multibody dynamic simulation of human movements, with applications in clinical practice, medical and assistive device design, sports, and industrial ergonomics. Multibody models of human neuromusculoskeletal systems are reviewed, including models of joints, contacts, and muscle forces or torques, followed by a review of simulation approaches that use optimal control methods and a cost function to predict human movements. Modelling and optimal control software are also reviewed, and directions for future research are suggested.

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“…For instance, the direct collocation method has shown its efficiency in some studies investigating human motion [5], [6]. It consists in approximating the integration of the system dynamics using polynomials that describe the state and control trajectories.…”

Section: Introductionmentioning

confidence: 99%

Bioptim, a Python framework for Musculoskeletal Optimal Control in Biomechanics

Michaud¹,

Bailly²,

Charbonneau³

et al. 2021

Preprint

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Musculoskeletal simulations are useful in biomechanics to investigate the causes of movement disorder, to estimate non-measurable physiological quantities or to study the optimality of human movement. We introduce bioptim, an easy-to-use Python framework for biomechanical optimal control, handling musculoskeletal models. Relying on algorithmic differentiation and the multiple shooting formulation, bioptim interfaces nonlinear solvers to quickly provide dynamically consistent optimal solutions. The software is both computationally efficient (C++ core) and easily customizable, thanks to its Python interface. It allows to quickly define a variety of biomechanical problems such as motion tracking/prediction, muscle-driven simulations, parameters optimization, multiphase problems, etc. It is also intended for real-time applications such as moving horizon estimation and model predictive control. Six contrasting examples are presented, comprising various models, dynamics, objective functions and constraints. They include data-driven simulations (i.e., a multiphase muscle driven gait cycle and an upper-limb real-time moving horizon estimation of muscle forces) and predictive simulations (i.e., a muscle-driven pointing task, a twisting somersault with a quaternion-based model, a position controller using external forces, and a multiphase torque-driven maximum-height jump motion).

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Comparison of direct collocation optimal control to trajectory optimization for parameter identification of an ellipsoidal foot–ground contact model

Cited by 13 publications

References 41 publications

Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach

Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach

Predictive multibody dynamic simulation of human neuromusculoskeletal systems: a review

Bioptim, a Python framework for Musculoskeletal Optimal Control in Biomechanics

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