Introduction - Human Motion Simulation: Predictive Dynamics - page 4

Predictive Dynamics
1. Determine:
Joint Profiles
2. Minimize:
Energy and Effort
3. Subject to:
—Joint Limits
—Physical constraints
—Other constraints
— Equations of motion
FIGURE 1.2
The general formulation for PD illustrated as a three-step optimization formulation.
regression model that should, in principle, predict motion. There are many obvi-
ous problems with this method, including:
￿ Difficulties in collecting the data for varying anthropometries. This includes
the changes of masses, moments of inertia, muscle performance, and many
other parameters for each person.
￿ Difficulties in managing a large number of parameters in a functional or
nonlinear regression algorithm. A large number of parameters means a
complex and less accurate model.
￿ Difficulties in predicting postures and motions for reaches that have obstacles.
For each obstacle, the experiments must be repeated.
￿ Difficulties in predicting motions where dynamics (external forces and loads)
play an important role.
After the apple fell from the tree on Newton's head, he proceeded to measure a
few more, came up with the general theory, and finally came up with a rigorous
mathematical formulation for all falling objects and, furthermore, for all objects in
motion. He did not measure every apple on every tree to come up with a theory
that works and that is the fundamental basis for all motion in our universe.
The idea of recording every motion for thousands of people and for thousands
of different scenarios does provide a good way to study motion and to validate
motion predicted with various methods. However, it has no value for the predic-
tion of motions beyond static postures.
1.4 Concluding remarks
This topic deals with the science of human motion. It presents a rigorous meth-
odology for representing human biomechanics and joint motion, and includes