Reading Notes

1. Create a model (muskuloskeletal, etc.)
2. Find a set of muscle excitations that generate the desired movement
3. Test the model by comparing results to experimental data
4. Analyze the simulation to answer research questions

The author present a somewhat simple musculoskeletal model where limbs are connected by typical joints (hinge, universal, ball-in-socket). They also present a simple but interesting muscle model that establish a relationship between:

• muscle excitation,
• muscle activations,
• muscle forces,
• musculotendon lengths and shortening velocities,
• joint torques,
• generalized coordinates and their derivatives.

The authors use dynamic optimization with a heuristic goal. The goal is to minimize metabolic energy expenditure per unit distance traveled, penalty terms are added to discourage hyperextended joints.

Three major challenges of future work are highlighted:

• Modeling challenges
  • Simulations often sensitive to data accuracy
  • More accurate musculoskeletal geometry and minematics
  • Improve equations for musculotendon dynamics
• Simulation challenges
  • Dynamic optimization incur great computational expense (hours on 5000 processors, parallel supercomputers)
  • At best, a solution obtained in a few days or a week
  • Need efficient algorithms for subject-specific simulations
  • Adaptation of robotics control technique are promising
  • Biggest limitation: exclusion of nervous system / open-loop simulation / no reflexes.
• Analysis challenges
  • Better way to identify which muscles are responsible for an observed ground reaction force
  • Sensitivity studies to evaluate how an analysis is sensitive to the model parameters

• I should try to understand the various concepts used in the muscle model.
• What exactly is meant by dynamic optimization?
• The paper contains many references to different sources of experimental data.
• A large part of the paper is devoted to exploring how a muscle-based simulation can be used to analyze gait information that would otherwise be hard to obtain.
• A long list of contributions due to progress in muscle-based simulation is given.

Reading Comments

Anderson F.C., Arnold A.S., Pandy M.G., Goldberg S.R., and Delp S.L. (in press). Simulation of Walking. In Rose, J. and Gamble, J.G. (eds.): Human Walking, 3rd Edition. Lippincott Williams & Williams, Inc., Philadelphia. 2005

Summary

Aims towards a dynamic simulation of walking that integrates facts about the anatomy and physiology of the neuromusculoskeletal system and the mechanics of multi-joint movement to provide a framework to reveal the cause-effect relationships between neuromuscular excitation patterns, muscle forces, ground reaction forces, and motions of the body.

Comments

-- PhilippeBeaudoin - 13 Feb 2009