Concluding Remarks - Human Motion Simulation: Predictive Dynamics - page 240

modeling, consider a soldier performing a task that requires significant strength
and courage. The psychological status of this soldier at the time of execution will
have a significant effect on the task performance.
Moreover, while PD accounts for varying strength capabilities, the internal
physiological state of a person must also be modeled. This includes the person's
medical condition, energy capability such as VO2 max, cardiovascular condition,
thermal condition, respiratory condition, etc.
Coupling of the psychological and physiological models with PD to create a
more comprehensive human simulator will be a necessary step for future advance-
ment of human simulation.
10.3.4 Modeling with a high level of fidelity
The modeling method presented in this topic uses the DH parameterization
method, recursive dynamics, and the PD approach to predict cause and effect.
The equations of motion in this formulation were accurately represented, albeit
some terms were also removed from the computational implementation. Here are
some areas to work on in order to improve or increase the level of fidelity for the
PD results.
a. Increase the number of DOF representing each joint.
b. Increase the degree of coupling between joints that are coupled.
c. Incorporate a more accurate biomechanical model at each joint. For example,
rather than modeling the knee joint as one DOF with a specific ROM, it is
possible to investigate a more accurate kinematic model in which ligaments
are represented and the resulting motion is correspondingly specified.
d. A more detailed model of the musculoskeletal system. While the PD approach
employs a joint-space representation of the skeletal system whereby muscle
action is represented by a resultant torque, muscle interaction and recruitment
are not modeled.
10.3.5 Real-time simulation
The goal of creating a human simulator that can respond to real and active forces
and moments, that can interact with researchers, and that has biomechanics that
can be monitored is close and within reach. While PD offers a broad underlying
formulation for achieving this goal, there are still several obstacles. One obstacle
is the inability to execute PD in real time because it is so computationally inten-
sive. Both the equations of motion and the optimization process are numerically
costly. Over the years, PD has been expedited by changing the algorithm to a fas-
ter code, by advancing the computational platforms, and by parallelizing some of
the code structure. Even though simulation of some of the tasks has been reduced
to a few minutes on a fast CPU, the main obstacle is in further parallelizing the