Rohrer Brandon, Hogan Neville
Cybernetic Systems Integration Department, Intelligent Systems and Robotics Center, Sandia National Laboratories, Albuquerque, NM, USA.
Biol Cybern. 2006 May;94(5):409-14. doi: 10.1007/s00422-006-0055-y. Epub 2006 Mar 29.
Evidence for the existence of discrete submovements underlying continuous human movement has motivated many attempts to "extract" them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. In previous work, a branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization, and hence, capable of avoiding spurious decompositions, was presented [Rohrer and Hogan (Biol Cybern 39:190-199, 2003)]. Here, we present a scattershot-type global nonlinear minimization algorithm that requires approximately four orders of magnitude less time to compute. A sensitivity analysis reveals that the scattershot algorithm can reliably detect changes in submovement parameters over time, e.g., over the course of neuromotor recovery.
连续人类运动背后存在离散子运动的证据促使人们进行了许多“提取”它们的尝试。尽管这些尝试产生了视觉上令人信服的结果，但所有已采用的方法都容易产生虚假的分解。在之前的工作中，提出了一种用于子运动提取的分支定界算法，该算法能够进行全局非线性最小化，因此能够避免虚假分解[Rohrer和Hogan（《生物控制论》39:190 - 199，2003）]。在此，我们提出一种散弹式全局非线性最小化算法，其计算时间大约少四个数量级。敏感性分析表明，散弹式算法能够可靠地检测子运动参数随时间的变化，例如在神经运动恢复过程中的变化。
