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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >A Globally Optimal Estimator for the Delta-Lognormal Modeling of Fast Reaching Movements
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A Globally Optimal Estimator for the Delta-Lognormal Modeling of Fast Reaching Movements

机译：快速到达运动的Delta对数正态建模的全局最优估计器

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摘要

Fast reaching movements are an important component of our daily interaction with the world and are consequently under investigation in many fields of science and engineering. Today, useful models are available for such studies, with tools for solving the inverse dynamics problem involved by these analyses. These tools generally provide a set of model parameters that allows an accurate and locally optimal reconstruction of the original movements. Although the solutions that they generate may provide a data curve fitting that is sufficient for some pattern recognition applications, the best possible solution is often necessary in others, particularly those involving neuroscience and biomedical signal processing. To generate these solutions, we present a globally optimal parameter extractor for the delta-lognormal modeling of reaching movements based on the branch-and-bound strategy. This algorithm is used to test the impact of white noise on the delta-lognormal modeling of reaching movements and to benchmark the state-of-the-art locally optimal algorithm. Our study shows that, even with globally optimal solutions, parameter averaging is important for obtaining reliable figures. It concludes that physiologically derived rules are necessary, in addition to global optimality, to achieve meaningful $DeltaLambda$ extractions which can be used to investigate the control patterns of these movement primitives.
机译：快速运动是我们与世界日常互动的重要组成部分，因此正在科学和工程学的许多领域进行调查。如今，有用的模型可用于此类研究，并具有解决这些分析涉及的逆动力学问题的工具。这些工具通常提供一组模型参数，允许对原始运动进行精确且局部最优的重构。尽管它们生成的解决方案可以提供足以满足某些模式识别应用程序的数据曲线拟合，但在其他解决方案中，尤其是涉及神经科学和生物医学信号处理的解决方案中，通常往往需要最佳解决方案。为了生成这些解决方案，我们提出了一种全局最优参数提取器，用于基于分支定界策略的到达运动的对数对数正态建模。该算法用于测试白噪声对到达运动的对数对数正态建模的影响，并确定最先进的局部最优算法的基准。我们的研究表明，即使使用全局最优解，参数平均对于获取可靠数据也很重要。结论是，除了全局最优性之外，生理上衍生的规则对于实现有意义的$ DeltaLambda $提取是必要的，该提取可用于研究这些运动原语的控制模式。

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