Comparison of different optimal control formulations for generating dynamically consistent crutch walking simulations using a torque-driven model

Febrer-Nafría, Míriam; Pallarès-López, Roger; Fregly, Benjamin J.; Font-Llagunes, Josep M.

doi:10.1016/j.mechmachtheory.2020.104031

Cited by 21 publications

(17 citation statements)

References 44 publications

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“…In a clinical context, where classical motion capture systems are often too demanding to be used, such a method could still give relevant results with less accurate kinematic data (Xsens, Leap Motion), enhanced by EMG signals. In the present work, we chose to work with markers according to the recommendations from Bélaise et al ( 2018b ), but a recent study suggests that tracking joint angles instead of markers improves convergence (Febrer-Nafría et al, 2020 ). In terms of solving speed, the outcomes of such a change in the tracked data should be investigated further since, if it simplifies the kinematic tracking term, turning the non-linear quadratic program into a linear one, it requires a round of inverse kinematics.…”

Section: Discussionmentioning

confidence: 99%

Real-Time and Dynamically Consistent Estimation of Muscle Forces Using a Moving Horizon EMG-Marker Tracking Algorithm—Application to Upper Limb Biomechanics

Bailly

Ceglia

Michaud

et al. 2021

Front. Bioeng. Biotechnol.

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Real-time biofeedback of muscle forces should help clinicians adapt their movement recommendations. Because these forces cannot directly be measured, researchers have developed numerical models and methods informed by electromyography (EMG) and body kinematics to estimate them. Among these methods, static optimization is the most computationally efficient and widely used. However, it suffers from limitation, namely: unrealistic joint torques computation, non-physiological muscle forces estimates and inconsistent for motions inducing co-contraction. Forward approaches, relying on numerical optimal control, address some of these issues, providing dynamically consistent estimates of muscle forces. However, they result in a high computational cost increase, apparently disqualifying them for real-time applications. However, this computational cost can be reduced by combining the implementation of a moving horizon estimation (MHE) and advanced optimization tools. Our objective was to assess the feasibility and accuracy of muscle forces estimation in real-time, using a MHE. To this end, a 4-DoFs arm actuated by 19 Hill-type muscle lines of action was modeled for simulating a set of reference motions, with corresponding EMG signals and markers positions. Excitation- and activation-driven models were tested to assess the effects of model complexity. Four levels of co-contraction, EMG noise and marker noise were simulated, to run the estimator under 64 different conditions, 30 times each. The MHE problem was implemented with three cost functions: EMG-markers tracking (high and low weight on markers) and marker-tracking with least-squared muscle excitations. For the excitation-driven model, a 7-frame MHE was selected as it allowed the estimator to run at 24 Hz (faster than biofeedback standard) while ensuring the lowest RMSE on estimates in noiseless conditions. This corresponds to a 3,500-fold speed improvement in comparison to state-of-the-art equivalent approaches. When adding experimental-like noise to the reference data, estimation error on muscle forces ranged from 1 to 30 N when tracking EMG signals and from 8 to 50 N (highly impacted by the co-contraction level) when muscle excitations were minimized. Statistical analysis was conducted to report significant effects of the problem conditions on the estimates. To conclude, the presented MHE implementation proved to be promising for real-time muscle forces estimation in experimental-like noise conditions, such as in biofeedback applications.

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Section: Discussionmentioning

confidence: 99%

Real-Time and Dynamically Consistent Estimation of Muscle Forces Using a Moving Horizon EMG-Marker Tracking Algorithm—Application to Upper Limb Biomechanics

Bailly

Ceglia

Michaud

et al. 2021

Front. Bioeng. Biotechnol.

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“…Despite the advantages of torque-driven models, this approach mostly [116,117] restricts individual-specific forward-dynamics simulation models to two-dimensions. At present, a technique for determining and describing individual-specific maximal torque profiles at joints such as the hip and shoulder collectively about three axes (e.g., flexionextension, abduction-adduction, internal-external rotation) has not been established.…”

Section: Strength Constraintsmentioning

confidence: 99%

A Review of Forward-Dynamics Simulation Models for Predicting Optimal Technique in Maximal Effort Sporting Movements

2021

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The identification of optimum technique for maximal effort sporting tasks is one of the greatest challenges within sports biomechanics. A theoretical approach using forward-dynamics simulation allows individual parameters to be systematically perturbed independently of potentially confounding variables. Each study typically follows a four-stage process of model construction, parameter determination, model evaluation, and model optimization. This review critically evaluates forward-dynamics simulation models of maximal effort sporting movements using a dynamical systems theory framework. Organismic, environmental, and task constraints applied within such models are critically evaluated, and recommendations are made regarding future directions and best practices. The incorporation of self-organizational processes representing movement variability and “intrinsic dynamics” remains limited. In the future, forward-dynamics simulation models predicting individual-specific optimal techniques of sporting movements may be used as indicative rather than prescriptive tools within a coaching framework to aid applied practice and understanding, although researchers and practitioners should continue to consider concerns resulting from dynamical systems theory regarding the complexity of models and particularly regarding self-organization processes.

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“…where T i with i ∈ [1,2,3,4,5] are the final times of the i th phase respectively, and T 0 = 0; ω h = −100 is the weight of the jump height term defined negative to maximize it; ω t = 0.1, ω sd = 0.1 and ω x = 1.0 are the weights of their respective objective functions;q is the generalized velocities part of the state vector x; andx is the state vector excluding the translations of the root segment. Thex * corresponds to a reference static position of the avatar with its knee slightly flexed and its arms horizontal.…”

Section: F Multiphase Vertical Jumpermentioning

confidence: 99%

“…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 different optimal control formulations for generating dynamically consistent crutch walking simulations using a torque-driven model

Cited by 21 publications

References 44 publications

Real-Time and Dynamically Consistent Estimation of Muscle Forces Using a Moving Horizon EMG-Marker Tracking Algorithm—Application to Upper Limb Biomechanics

Real-Time and Dynamically Consistent Estimation of Muscle Forces Using a Moving Horizon EMG-Marker Tracking Algorithm—Application to Upper Limb Biomechanics

A Review of Forward-Dynamics Simulation Models for Predicting Optimal Technique in Maximal Effort Sporting Movements

Bioptim, a Python framework for Musculoskeletal Optimal Control in Biomechanics

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