The Kinematic Theory of rapid movements and its associated Sigma-Lognormal model have been extensively used in a large variety of applications. While the physical and biological meaning of the model have been widely tested and validated for rapid movements, some shortcomings have been detected when it is used with continuous long and complex movements. To alleviate such drawbacks, and inspired by the motor equivalence theory and a conceivable visual feedback, this paper proposes a novel framework to extract the Sigma-Lognormal parameters, namely iDeLog. Specifically, iDeLog consists of two steps. The first one, influenced by the motor equivalence model, separately derives an initial action plan defined by a set of virtual points and angles from the trajectory and a sequence of lognormals from the velocity. In the second step, based on a hypothetical visual feedback compatible with an open-loop motor control, the virtual target points of the action plan are iteratively moved to improve the matching between the observed and reconstructed trajectory and velocity. During experiments conducted with handwritten signatures, iDeLog obtained promising results as compared to the previous development of the Sigma-Lognormal.

中文翻译：

---

iDeLog：Sigma对数正态参数的迭代双重空间和运动学提取

快速运动的运动学理论及其相关的Sigma-Lognormal模型已广泛用于各种应用中。尽管该模型的物理和生物学含义已针对快速运动进行了广泛的测试和验证，但在将其用于连续的长而复杂的运动时却发现了一些缺点。为了减轻此类缺陷，并在运动等效理论和可能的视觉反馈的启发下，本文提出了一种新颖的框架来提取Sigma-Lognormal参数，即iDeLog。具体来说，iDeLog包含两个步骤。第一个受电动机等效模型的影响，分别得出初始行动计划，该行动计划由轨迹的一组虚拟点和角度以及速度的对数法线序列定义。第二步 基于与开环电动机控制兼容的假设视觉反馈，可重复移动行动计划的虚拟目标点，以改善观察到的和重建的轨迹与速度之间的匹配。在用手写签名进行的实验中，与以前开发的Sigma-Lognormal相比，iDeLog获得了可喜的结果。