Lan N
Department of Electrical Engineering, Tsinghua University, Beijing, People's Republic of China.
Biol Cybern. 1997 Feb;76(2):107-17. doi: 10.1007/s004220050325.
In this paper, we propose a model of biological motor control for generation of goal-directed multi-joint arm movements, and study the formation of muscle control inputs and invariant kinematic features of movements. The model has a hierarchical structure that can determine the control inputs for a set of redundant muscles without any inverse computation. Calculation of motor commands is divided into two stages, each of which performs a transformation of motor commands from one coordinate system to another. At the first level, a central controller in the brain accepts instructions from higher centers, which represent the motor goal in the Cartesian space. The controller computes joint equilibrium trajectories and excitation signals according to a minimum effort criterion. At the second level, a neural network in the spinal cord translates the excitation signals and equilibrium trajectories into control commands to three pairs of antagonist muscles which are redundant for a two-joint arm. No inverse computation is required in the determination of individual muscle commands. The minimum effort controller can produce arm movements whose dynamic and kinematic features are similar to those of voluntary arm movements. For fast movements, the hand approaches a target position along a near-straight path with a smooth bell-shaped velocity. The equilibrium trajectories in X and Y show an "N' shape, but the end-point equilibrium path zigzags around the hand path. Joint movements are not always smooth. Joint reversal is found in movements in some directions. The excitation signals have a triphasic (or biphasic) pulse pattern, which leads to stereotyped triphasic (or biphasic) bursts in muscle control inputs, and a dynamically modulated joint stiffness. There is a fixed sequence of muscle activation from proximal muscles to distal muscles. The order is preserved in all movements. For slow movements, it is shown that a constant joint stiffness is necessary to produce a smooth movement with a bell-shaped velocity. Scaled movements can be reproduced by varying the constraints on the maximal level of excitation signals according to the speed of movement. When the inertial parameters of the arm are altered, movement trajectories can be kept invariant by adjusting the pulse height values, showing the ability to adapt to load changes. These results agree with a wide range of experimental observations on human voluntary movements.
在本文中,我们提出了一种用于生成目标导向的多关节手臂运动的生物运动控制模型,并研究了肌肉控制输入的形成以及运动的不变运动学特征。该模型具有层次结构,无需任何逆计算就能确定一组冗余肌肉的控制输入。运动指令的计算分为两个阶段,每个阶段都执行运动指令从一个坐标系到另一个坐标系的转换。在第一级,大脑中的中央控制器接收来自更高层级中枢的指令,这些指令在笛卡尔空间中表示运动目标。控制器根据最小努力准则计算关节平衡轨迹和兴奋信号。在第二级,脊髓中的神经网络将兴奋信号和平衡轨迹转换为针对三对拮抗肌的控制指令,对于双关节手臂而言这些肌肉是冗余的。在确定单个肌肉指令时无需进行逆计算。最小努力控制器能够产生其动力学和运动学特征与自愿手臂运动相似的手臂运动。对于快速运动,手沿着接近直线的路径以平滑的钟形速度接近目标位置。X和Y方向的平衡轨迹呈“N”形,但端点平衡路径在手路径周围呈锯齿状。关节运动并不总是平滑的。在某些方向的运动中会出现关节反转。兴奋信号具有三相(或双相)脉冲模式,这导致肌肉控制输入中出现刻板的三相(或双相)爆发以及动态调制的关节刚度。从近端肌肉到远端肌肉存在固定的肌肉激活顺序。该顺序在所有运动中都得以保留。对于缓慢运动,结果表明需要恒定的关节刚度才能产生具有钟形速度的平滑运动。通过根据运动速度改变对兴奋信号最大水平的约束,可以再现缩放运动。当手臂的惯性参数改变时,通过调整脉冲高度值可以保持运动轨迹不变,这表明具有适应负载变化的能力。这些结果与关于人类自愿运动的广泛实验观察结果一致。