Optimization of Muscle Wrapping Objects Using Simulated Annealing

Musculoskeletal models use wrapping objects to constrain muscle paths from passing through anatomical obstacles; however, the selection of wrapping object parameters is typically a manual, iterative, and time-consuming process. The purpose of this study was to use a data-driven optimization algorithm to determine wrapping object parameters. Wrapping parameters were determined using simulated annealing for two cases: (1) modeling the triceps at the elbow using a cylindrical wrapping object, and (2) modeling the middle deltoid using a spherical wrapping object. It was found that an optimization algorithm could be used to determine wrapping object parameters which produced moment arms that were similar to experimental data. The greatest benefit of this method is the efficiency at which model parameters were determined, thus eliminating much of the time required to manually refine the wrapping objects. Model development could be further improved by extending this method to other model parameters and combining various optimization techniques.

[1]  S L Delp,et al.  A graphics-based software system to develop and analyze models of musculoskeletal structures. , 1995, Computers in biology and medicine.

[2]  R. L. Linscheid,et al.  Muscles across the elbow joint: a biomechanical analysis. , 1981, Journal of biomechanics.

[3]  J. Saunders,et al.  Observations of the Function of the Shoulder Joint , 1996, Clinical orthopaedics and related research.

[4]  A G Feldman,et al.  Moment arms and lengths of human upper limb muscles as functions of joint angles. , 1996, Journal of biomechanics.

[5]  Marcus G Pandy,et al.  Moment arms of the muscles crossing the anatomical shoulder , 2008, Journal of anatomy.

[6]  F.C.T. Helm,et al.  The shoulder mechanism: a dynamic approach , 1991 .

[7]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[8]  K. An,et al.  Shoulder muscle moment arms during horizontal flexion and elevation. , 1997, Journal of shoulder and elbow surgery.

[9]  W. Smutz,et al.  Roles of deltoid and rotator cuff muscles in shoulder elevation. , 1997, Clinical biomechanics.

[10]  M Gerbeaux,et al.  Musculo-articular modelling of the triceps brachii. , 1996, Journal of biomechanics.

[11]  Diana B. Petitti,et al.  Meta-Analysis, Decision Analysis, and Cost-Effectiveness Analysis: Methods for Quantitative Synthesis in Medicine , 1994 .

[12]  Scott L. Delp,et al.  A Model of the Upper Extremity for Simulating Musculoskeletal Surgery and Analyzing Neuromuscular Control , 2005, Annals of Biomedical Engineering.

[13]  Richard A. Lasher,et al.  Defining and evaluating wrapping surfaces for MRI-derived spinal muscle paths. , 2008, Journal of biomechanics.

[14]  G. Ettema,et al.  The moment arms of 23 muscle segments of the upper limb with varying elbow and forearm positions: Implications for motor control , 1998 .

[15]  M. Pandy,et al.  The Obstacle-Set Method for Representing Muscle Paths in Musculoskeletal Models , 2000, Computer methods in biomechanics and biomedical engineering.

[16]  A Bayesian approach to biomechanical modeling to optimize over large parameter spaces while considering anatomical variability , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Francisco J. Valero Cuevas,et al.  Beyond Parameter Estimation: Extending Biomechanical Modeling by the Explicit Exploration of Model Topology , 2007, IEEE Transactions on Biomedical Engineering.

[18]  S. Delp,et al.  Scaling of peak moment arms of elbow muscles with upper extremity bone dimensions. , 2002, Journal of biomechanics.

[19]  Alex J. Sutton,et al.  Methods for Meta-Analysis in Medical Research , 2000 .