Edelman S, Flash T
Biol Cybern. 1987;57(1-2):25-36. doi: 10.1007/BF00318713.
The research reported here is concerned with hand trajectory planning for the class of movements involved in handwriting. Previous studies show that the kinematics of human two-joint arm movements in the horizontal plane can be described by a model which is based on dynamic minimization of the square of the third derivative of hand position (jerk), integrated over the entire movement. We extend this approach to both the analysis and the synthesis of the trajectories occurring in the generation of handwritten characters. Several basic strokes are identified and possible stroke concatenation rules are suggested. Given a concise symbolic representation of a stroke shape, a simple algorithm computes the complete kinematic specification of the corresponding trajectory. A handwriting generation model based on a kinematics from shape principle and on dynamic optimization is formulated and tested. Good qualitative and quantitative agreement was found between subject recordings and trajectories generated by the model. The simple symbolic representation of hand motion suggested here may permit the central nervous system to learn, store and modify motor action plans for writing in an efficient manner.
本文所报道的研究关注的是手写过程中涉及的一类运动的手部轨迹规划。先前的研究表明,人体双关节手臂在水平面内运动的运动学可以用一个模型来描述,该模型基于对手部位置三阶导数(加加速度)平方在整个运动过程中的动态最小化。我们将这种方法扩展到手写字符生成过程中出现的轨迹的分析和合成。识别出了几种基本笔画,并提出了可能的笔画连接规则。给定笔画形状的简洁符号表示,一种简单的算法就能计算出相应轨迹的完整运动学规范。基于形状原理的运动学和动态优化,构建并测试了一个手写生成模型。在受试者记录与模型生成的轨迹之间发现了良好的定性和定量一致性。这里提出的手部运动的简单符号表示可能使中枢神经系统能够以高效的方式学习、存储和修改书写的运动动作计划。