Eu – Fp7
نویسندگان
چکیده
A recent study has shown that geometrical invariance can describe the kinematic features of human drawing and locomotion movements [1]. The proposed theory has suggested that movement duration and compositionality arise from the cooperation among the Euclidian, equi-affine and full affine geometries. On the other hand, past studies have shown that different optimization principles can be used to account for different human movements. The suggested optimization principles included the minimum jerk principle which was suggested for drawing and locomotion movements, the minimization of acceleration for locomotion movements, and the minimum variance and optimal feedback control principles suggested for the modeling of a variety of motor tasks. In the present study the velocity profiles for movement trajectories along prescribed paths are predicted based on several optimization principles. We examine what specific geometrical combinations yield the optimal performance for the chosen optimization principles. We show that the kinematic features of human drawing movements can be predicted using the minimum-jerk criterion as the optimization function and use this model as a guideline for selecting appropriate geometrical combinations. On the other hand the minimum-acceleration principle is more appropriate for accounting for the observed velocity profiles for human locomotion. These optimization principles are also used to infer movement segmentation for drawing and locomotion trajectories and to predict the durations of the derived segments. This study leaves open the questions of how the movement paths are selected and the relationships between the suggested optimization principles and motor learning of new tasks.
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تاریخ انتشار 2011