论文标题
继承泰勒系列的关系光谱：肌肉协同和手联轴
A Relation Spectrum Inheriting Taylor Series: Muscle Synergy and Coupling for Hand
论文作者
论文摘要
数学中有两种著名的函数分解方法：泰勒系列和傅立叶系列。傅立叶序列发展为傅立叶光谱，用于信号分解\分析。但是，由于无法解决没有明确功能表达的功能的泰勒序列，因此泰勒系列很少用于工程。在这里，我们通过树突网开发了Taylor系列，构建了一个关系谱，并将其应用于模型或系统分解\分析。特定的工程：对肌肉活动与手指运动之间直观联系的知识对于设计不需要用户预训练的商业假肢的设计至关重要。但是，由于人类的复杂性，这种联系尚未理解。在这项研究中，将关系谱应用于分析肌肉手指系统。一个单一肌肉会使多个手指纵向或多个肌肉同时致动一根手指。因此，该研究是在肌肉协同作用和肌肉耦合方面进行的。本文有两个主要贡献。 （1）手的发现有助于设计假肢。 （2）关系频谱使在线模型人类阅读使在线绩效和离线结果统一。代码（大多数字段的新颖工具）可在https://github.com/liugang1234567/gang-neuron上找到。
There are two famous function decomposition methods in math: Taylor Series and Fourier Series. Fourier series developed into Fourier spectrum, which was applied to signal decomposition\analysis. However, because the Taylor series whose function without a definite functional expression cannot be solved, Taylor Series has rarely been used in engineering. Here, we developed Taylor series by our Dendrite Net, constructed a relation spectrum, and applied it to model or system decomposition\analysis. Specific engineering: the knowledge of the intuitive link between muscle activity and the finger movement is vital for the design of commercial prosthetic hands that do not need user pre-training. However, this link has yet to be understood due to the complexity of human hand. In this study, the relation spectrum was applied to analyze the muscle-finger system. One single muscle actuates multiple fingers, or multiple muscles actuate one single finger simultaneously. Thus, the research was in muscle synergy and muscle coupling for hand. This paper has two main contributions. (1) The findings of hand contribute to designing prosthetic hands. (2) The relation spectrum makes the online model human-readable, which unifies online performance and offline results. Code (novel tool for most fields) is available at https://github.com/liugang1234567/Gang-neuron.
