Biomedical Engineering Reference
In-Depth Information
mathematically capable of predicting intermittent tasks as well as dynamic tasks
for use with predictive dynamics.
6.8
Strength and fatigue interaction
One method to combine strength and fatigue models for DHM is to use the fatigue
model to decay the corresponding strength surface (e.g., joint-specific). For exam-
ple, as the elbow flexors are used (task modeled in DHM to provide task intensity
input to fatigue model), the fatigue model can then predict the decay in peak strength
capability over time. This can be conceptualized as a fatigue coefficient with
values from 0 (completely fatigued) to 1 (completely rested). The product of this
coefficient with the peak 3D strength surfaces results in the time-varying strength
properties of a joint as a function of time (and the underlying task involved).
Continued efforts are needed to validate and determine the practical applica-
tions of these modeling approaches, but significant advances in modeling strength
and fatigue have occurred in the past decade. Future advances are likely to out-
pace previous accomplishments, making this area an exciting component in the
development of DHM tools.
6.9
Concluding remarks
In summary, representing strength and fatigue for digital human models can be
accomplished with surprising accuracy at the joint level, which we call the joint
space. By applying both well-documented musculoskeletal relationships with new
data and modeling approaches, numerous muscle force nonlinearities can be effi-
ciently modeled computationally.
Modeling muscle forces as the limiting factor for a particular motion is both dif-
ficult and impractical, whereas joint-space strength limits provide a computationally
efficient approach. Predicted joint torques obtained from predictive dynamics can
readily be compared against normative torques (strength percentile surfaces, post-
processing) or the 3D strength surfaces can be used as constraints (pre-processing),
which better limit the capability of a human model to perform a task.
Undoubtedly, future advances will continue to improve the accuracy of these
modeling approaches, making digital humans increasingly realistic and useful
tools for a wide variety of applications.
References
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