Using a V-Shaped Learning Algorithm to Predict Stability Control on Curved Objects

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2017-01-31
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en
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In this study, research was done on the model for motor learning as described by Franklin et al. (2008) to see whether the model was able to predict stability on curved objects. The results from the model were promising. However, the model was based on a task that involved performing a forward reaching movement in different force fields, such as a divergent field, which is based on external forces. In our research we looked if this model was able to predict stability in a situation where the instability came from task geometry and signal-dependent noise. In the simulation, three different object with varying curvatures were simulated. The simulation started inside the object, 1cm to be precise, and the goal for the simulation was to remain in that position. When inside the object a force of 10N was exerted onto the subject to push it out of the object. The subject had to therefore, produce a net force in the muscles to counteract this force. Because the simulation was dealing with curvatures the direction of the force was dependent upon the location of the hand. Our results show that when the curvature increases the stiffness also increases. The direction of stiffness ellipse turned towards the direction of the instability (as was concluded in research by Selen et al. (2009)). However, this rotation was mirrored to the rotation from the results by Selen et al. (2009).
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Faculteit der Sociale Wetenschappen