| Summary: | Digital human models are more and more frequently employed in product
development processes to take human factors into account since the earliest
stages of product design. To simulate the interaction of different user
populations with a variety of environments, human motion prediction is a
useful tool, as it aims at predicting the motion that a generic subject of a
user population would reasonably perform to carry out a specific task in a
given environment.
The motivation of the research work presented in this thesis is the
improvement of current motion prediction methods in terms of realism and
representativeness. On the one hand, dynamics is included in our
formulation, in order to yield physically sound predictions and in view of
the fact that the forces and torques acting on and within the human body
play a relevant role in discomfort perception. On the other, a hybrid
approach is followed, combining the advantages of both data-based
methods (which rely on actually performed motions for reference) and
knowledge-based methods (which rely on the identification of the motion
control laws underlying task-oriented motions).
First the method is introduced, and is then applied to the prediction
of clutch pedal depression motions. For this purpose, a database of clutch
pedal depressions was analysed to gain insight into the subject-related and
environment-related features that mostly affect the motion and into the
different behavioural patterns that people exhibit carrying out the task.
Both a qualitative and quantitative validation of our motion
prediction method are presented. The former consists in comparing the
most relevant kinematic and dynamic magnitudes in the motion against
actually performed motions; the latter is based on the definition of a novel
measure, which represents the realism and the representativeness of the
predicted motions, and which is compared to the inherent variability of
actually performed motions.
The results obtained show that the proposed motion prediction
method is a valid alternative to current methods, when both the physical
soundness and the realism of the motion are required in the prediction.
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