Due to the numerous degrees of freedom in the human motor system, there exists an infinite number of possible movements for any given task. Unfortunately, it is currently unknown how the human central nervous system (CNS) chooses one movement out of the plethora of possible movements to solve the task at hand. The purpose of this study is the construction of a computational model of human movement coordination to unravel the principles the CNS might use to select one movement from plethora of possible movements in a given situation. Thereby, different optimization criteria were examined. The comparison of predicted and measured movement patterns exhibited that a minimum jerk strategy on joint level yielded the closest fit to the human data.
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