Adaptation of Force and Impedance in a Simulated Arm
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2016-07-15
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en
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Abstract
Humans often stabilize objects by co-contraction,
also known as the activation of opposing muscles. With more
muscle activation there is also more limb stiffness. More muscle
activation requires more energy. Several studies suggested a linear
learning function for motor learning of movements, based on
kinematic error, either in joint or muscle space (for example
Kawato, Furukawa, and Suzuki (1987)). However, a V-shaped
learning function is preferable, as stated by Franklin et al. (2008).
What is the influence of the parameters of the V-shaped learning
function on energy and precision? The different values for the
parameters influence either energy or precision, so that these two
are in a trade-off. In this paper the model proposed by Franklin
et al. (2008) is implemented while using generated trajectory
data to run tests with varying values for the learning parameters
to gain a better insight into this trade-off. It is found that not
all parameters correlate with model precision. It is shown that
precision increases while at the same time the input energy
increases. Therefore we can conclude that there is a trade-off
between energy and precision. In general an increase of activation
leads to more precision and a decreased efficiency regarding
energy.
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Faculteit der Sociale Wetenschappen