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this study we employed an Artificial Neuronal Network to analyze the
forces flexing the monofin in reaction to water resistance. In addition
we selected and characterized key kinematic parameters of leg and
monofin movements that define how to use a monofin efficiently and
economically to achieve maximum swimming speed. By collecting the
data recorded by strain gauges placed throughout the monofin, we were
able to demonstrate the distribution of forces flexing the monofin
in a single movement cycle. Kinematic and dynamic data were synchronized
and used as entry variable to build up a Multi-Layer Perception Network.
The horizontal velocity of the swimmer's center of body mass was used
as an output variable. The network response graphs indicated the criteria
for achieving maximum swimming speed. Our results pointed out the
need to intensify the angular velocity of thigh extension and dorsal
flexion of the feet, to strengthen velocity of attack of the tail
and to accelerate the attack of the distal part of the fin. The other
two parameters which should be taken into account are dynamics of
tail flexion change in downbeat and dynamics of the change in angle
of attack in upbeat.
KEY
WORDS: Kinematics, dynamics, leg and fin movements, modeling.
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