Determining residual reduction algorithm kinematic tracking weights for a sidestep cut via numerical optimization

作者：Samaan Michael A^*; Weinhandl Joshua T; Bawab Sebastian Y; Ringleb Stacie I

来源：Computer Methods in Biomechanics and Biomedical Engineering, 2016, 19(16): 1721-1729.

DOI：10.1080/10255842.2016.1183123

• Particle swarm optimization
• simulated annealing
• sidestep cut
• opensim
• residual reduction algorithm (RRA)

Musculoskeletal modeling allows for the determination of various parameters during dynamic maneuvers by using in vivo kinematic and ground reaction force (GRF) data as inputs. Differences between experimental and model marker data and inconsistencies in the GRFs applied to these musculoskeletal models may not produce accurate simulations. Therefore, residual forces and moments are applied to these models in order to reduce these differences. Numerical optimization techniques can be used to determine optimal tracking weights of each degree of freedom of a musculoskeletal model in order to reduce differences between the experimental and model marker data as well as residual forces and moments. In this study, the particle swarm optimization (PSO) and simplex simulated annealing (SIMPSA) algorithms were used to determine optimal tracking weights for the simulation of a sidestep cut. The PSO and SIMPSA algorithms were able to produce model kinematics that were within 1.4 degrees of experimental kinematics with residual forces and moments of less than 10N and 18Nm, respectively. The PSO algorithm was able to replicate the experimental kinematic data more closely and produce more dynamically consistent kinematic data for a sidestep cut compared to the SIMPSA algorithm. Future studies should use external optimization routines to determine dynamically consistent kinematic data and report the differences between experimental and model data for these musculoskeletal simulations.

• 出版日期2016