Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
Hum Mov Sci. 2012 Oct;31(5):1037-55. doi: 10.1016/j.humov.2012.01.001. Epub 2012 Aug 25.
Flash and Hogan (1985) suggested that the CNS employs a minimum jerk strategy when planning any given movement. Later, Nakano et al. (1999) showed that minimum angle jerk predicts the actual arm trajectory curvature better than the minimum jerk model. Friedman and Flash (2009) confirmed this claim. Besides the behavioral support that we will discuss, we will show that this model allows simplicity in planning any given movement. In particular, we prove mathematically that each movement that satisfies the minimum joint angle jerk condition is reproducible by a linear combination of six functions. These functions are calculated independent of the type of the movement and are normalized in the time domain. Hence, we call these six universal functions the Movement Elements (ME). We also show that the kinematic information at the beginning and end of the movement determines the coefficients of the linear combination. On the other hand, in analyzing recorded data from sit-to-stand (STS) transfer, arm-reaching movement (ARM) and gait, we observed that minimum joint angle jerk condition is satisfied only during different successive phases of these movements and not for the entire movement. Driven by these observations, we assumed that any given ballistic movement may be decomposed into several successive phases without overlap, such that for each phase the minimum joint angle jerk condition is satisfied. At the boundaries of each phase the angular acceleration of each joint should obtain its extremum (zero third derivative). As a consequence, joint angles at each phase will be linear combinations of the introduced MEs. Coefficients of the linear combination at each phase are the values of the joint kinematics at the boundaries of that phase. Finally, we conclude that these observations may constitute the basis of a computational interpretation, put differently, of the strategy used by the Central Nervous System (CNS) for motor planning. We call this possible interpretation "Coordinated Minimum Angle jerk Policy" or COMAP. Based on this policy, the function of the CNS in generating the desired pattern of any given task (like STS, ARM or gait) can be described computationally using three factors: (1) the kinematics of the motor system at given body states, i.e., at certain movement events/instances, (2) the time length of each phase, and (3) the proposed MEs. From a computational point of view, this model significantly simplifies the processes of movement planning as well as feature abstraction for saving characterizing information of any given movement in memory.
Flash 和 Hogan(1985 年)提出,中枢神经系统在规划任何给定运动时采用最小冲击策略。后来,Nakano 等人(1999 年)表明,最小角度冲击比最小冲击模型更好地预测实际手臂轨迹曲率。Friedman 和 Flash(2009 年)证实了这一说法。除了我们将讨论的行为支持外,我们还将表明,该模型允许在规划任何给定运动时保持简单性。特别是,我们从数学上证明,满足最小关节角冲击条件的每个运动都可以通过六个函数的线性组合来再现。这些函数是独立于运动类型计算的,并在时域中归一化。因此,我们将这六个通用函数称为运动元素(ME)。我们还表明,运动开始和结束时的运动学信息决定了线性组合的系数。另一方面,在分析从坐立到站立(STS)转移、手臂伸展运动(ARM)和步态的记录数据时,我们观察到最小关节角冲击条件仅在这些运动的不同连续阶段得到满足,而不是在整个运动过程中得到满足。受这些观察结果的驱动,我们假设任何给定的弹道运动都可以分解为几个没有重叠的连续阶段,使得每个阶段都满足最小关节角冲击条件。在每个阶段的边界处,每个关节的角加速度应达到极值(零三阶导数)。因此,每个阶段的关节角度将是引入的 ME 的线性组合。每个阶段的线性组合系数是该阶段边界处关节运动学的值。最后,我们得出结论,这些观察结果可能构成中枢神经系统(CNS)用于运动规划的策略的计算解释的基础。我们将这种可能的解释称为“协调最小角度冲击策略”或 COMAP。基于该策略,使用三个因素可以计算地描述 CNS 在生成任何给定任务(如 STS、ARM 或步态)的期望模式的功能:(1)给定身体状态下的运动系统的运动学,即在某些运动事件/实例中,(2)每个阶段的持续时间,以及(3)提出的 ME。从计算的角度来看,该模型极大地简化了运动规划过程以及特征抽象,以便在记忆中保存任何给定运动的特征信息。