Dynamic simulation of human motion: numerically efficient inclusion of muscle physiology by convex optimization

Abstract

Determining the muscle forces that underlie some experimentally observed human motion, is a challenging biomechanical problem, both from an experimental and a computational point of view. No non-invasive method is currently available for experimentally measuring muscle forces. The alternative of computing them from the observed motion is complicated by the inherent overactuation of the human body: it has many more muscles than strictly needed for driving all the degrees of freedom of the skeleton. As a result, the skeleton's equations of motion do not suffice to determine the muscle forces unambiguously. Therefore, muscle force determination is often reformulated as a (large-scale) optimization problem.Generally, the optimization approaches are classified according to the formalism, inverse or forward, adopted for solving the skeleton's equations of motion. Classical inverse approaches are fast but do not take into account the constraints imposed by muscle physiology. Classical forward approaches, on the other hand, do take the muscle physiology into account but are extremely costly from a computational point of view.

Regularity Aspects in Inverse Musculoskeletal Biomechanics

“…The nonlinear least squares problem (3)(4) is solved by the Matlab^ function f mincon. This function uses the sequential quadratic programming method with approximated Hessian, i.e a quasi-Newton method.…”

“…The advantage with the inverse dynamics method is the possibihty to make the problem convex [3,4] and thereby no start guess is needed as in the forward dynamics method.…”

Review of EMG-based neuromuscular interfaces for rehabilitation: elbow joint as an example

“…This block simulates muscle as an active tissue, models the interaction between the muscle fibres and the tendon, and considers the mechanical properties of the tendon tissue (Pipeleers et al, 2007).…”

Estimation of muscular forces from SSA smoothed sEMG signals calibrated by inverse dynamics-based physiological static optimization

“…Therefore, optimization schemes are needed to solve such indeterminacy problem. Several optimization methods (static optimization, dynamic optimization, augmented static optimization) and optimization criteria (minimum metabolical cost of transport, minimum sum of muscle stresses, time-integral cost of activations, torque-tracking) are available in the literature [2][3][4][5][6][7]. The traditional ID computation method has been widely used to predict muscle forces but, unfortunately, the solution may not be physiologically consistent if static methods are used (where contraction dynamics is not considered), or may have a high computational cost for dynamic approaches.…”