Static and dynamic optimization solutions for gait are practically equivalent.

By Frank C. Anderson, Marcus G. Pandy. Journal of Biomechanics, 2001

808 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Orthopedics and Sports Medicine

View Paper: https://www.semanticscholar.org/paper/8be3e9fcdcbc6e8c5e728c423acb0dd0ddd4bd84

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In Biomedical Engineering

In Cognitive Neuroscience

In Orthopedics and Sports Medicine

Citations in the corpus

How to build a dinosaur: Musculoskeletal modeling and simulation of locomotor biomechanics in extinct animals (2020, background): “Although static and dynamic analyses can give comparable estimates of muscle activation patterns (Anderson and Pandy 2001; Lin et al. 2012), static analyses may underestimate absolute performance abilities.”
Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement. (2015, background): “Excluding the force–length–velocity relationship did not significantly change the muscle forces during walking predicted by static optimization [99], although many muscles have been shown to have a wide Fig.”
Predictive dynamics: an optimization-based novel approach for human motion simulation (2010, methodology): “1998; Anderson and Pandy 2001a). Anderson and Pandy (2001b) presented a model with 23 degrees of freedom (DOF) and 54 muscles for normal symmetric walking on level ground. The objective function was metabolic energy per unit distance, and the design variables were the muscle actuations. Equations of”
Contributions of the individual ankle plantar flexors to support, forward progression and swing initiation during walking. (2001, background): “Similarly, solving the force-sharing problem alone (e.g. Anderson and Pandy, 2001), like inverse dynamics-based analyses, does not provide insight into causal relationships between muscle activity and task performance.”
EMG-Driven Forward-Dynamic Estimation of Muscle Force and Joint Moment about Multiple Degrees of Freedom in the Human Lower Extremity (2012, methodology): “Using the software OpenSim [26], a generic model of the human musculoskeletal geometry [2] was scaled to match the individual subject’s size and body proportions.”
Model-based estimation of muscle forces exerted during movements. (2007, background): “Current movement prediction algorithms are extremely computer-intensive (Anderson and Pandy, 2001a,b) which necessitates parallel computing (Neptune, 1999; Anderson et al., 1995; van Soest and Casius, 2003; Koh et al., 2004).”
Generating dynamic simulations of movement using computed muscle control. (2003, methodology): “For the static optimization problem, the performance criterion (J) was chosen to be the sum of squared muscle activations (Anderson and Pandy, 2001):”
Gait Analysis Using Wearable Sensors (2012, methodology): “The static and dynamic optimization solution is a converse method to estimate muscle force for gait analysis [148].”
Individual muscle contributions to support in normal walking. (2003, methodology): “The analyses performed in the present study are based on a three-dimensional, muscle-actuated model of the body and a detailed dynamic optimization solution for normal gait [13,14].”
Resolving actuator redundancy - optimal control vs. control allocation (2005, background): “A related comparison between dynamic and static optimization for computing the actuator control distribution can be found in [5].”