继承泰勒级数的关系谱分析：手部肌肉协同与耦合

刘刚; 王晶

doi:10.1631/FITEE.2000578

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继承泰勒级数的关系谱分析：手部肌肉协同与耦合

常规文章 | Updated：2022-04-19

- 继承泰勒级数的关系谱分析：手部肌肉协同与耦合
  Enhanced Publication
- A relation spectrum inheriting Taylor series: muscle synergy and coupling for hand
- 信息与电子工程前沿（英文） 2022年23卷第1期页码：145-157
- Affiliations：
  
  Institute of Robotics and Intelligent Systems, Xi'an Jiaotong University, Xi'an710049, China
- Author bio：
  
  E-mail: gangliu.6677@gmail.com;
  ‡Corresponding author
- Funds：
  
  Science and Technology Project of Shaanxi Province, China(2019SF-109)
- DOI：10.1631/FITEE.2000578
  中图分类号： TB18;O173
- 纸质出版日期：2022-01-0 ，
  
  收稿日期：2020-10-26，
  
  录用日期：2021-04-15
- Accepted：
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刘刚, 王晶. 继承泰勒级数的关系谱分析：手部肌肉协同与耦合[J]. 信息与电子工程前沿（英文）, 2022,23(1):145-157.

GANG LIU, JING WANG. A relation spectrum inheriting Taylor series: muscle synergy and coupling for hand. [J]. Frontiers of information technology & electronic engineering, 2022, 23(1): 145-157.
刘刚, 王晶. 继承泰勒级数的关系谱分析：手部肌肉协同与耦合[J]. 信息与电子工程前沿（英文）, 2022,23(1):145-157. DOI： 10.1631/FITEE.2000578.

GANG LIU, JING WANG. A relation spectrum inheriting Taylor series: muscle synergy and coupling for hand. [J]. Frontiers of information technology & electronic engineering, 2022, 23(1): 145-157. DOI： 10.1631/FITEE.2000578.

摘要

数学中有两种著名的函数分解方法：泰勒级数和傅里叶级数。傅里叶级数发展成为傅里叶频谱，用于信号分解和分析；而泰勒级数的求解需要已知具体函数表达式，所以其在工程领域很少被应用。本文使用树突网络发展了泰勒级数，构造了关系谱，并将其应用于模型或系统分解和分析。了解肌肉激活与手指运动之间的直观联系对于开发无需用户预训练的商业假肢至关重要。然而，由于人手的复杂性，该直观联系尚未被理解。本文使用关系谱分析了肌肉—手指系统。在手指运动中，一块肌肉同时驱动多个手指，多块肌肉同时驱动一个手指。因此，本研究聚焦于手部的肌肉协同与耦合。本文有两个主要贡献：（1）有关手部的发现有助于假肢手的设计；（2）关系谱使在线模型可读，从而统一了在线性能和离线结果。开源代码见https://github.com/liugang1234567/Gang-neuron。

Abstract

There are two famous function decomposition methods in math: the Taylor series and the Fourier series. The Fourier series developed into the Fourier spectrum

which was applied to signal decomposition and analysis. However

because the Taylor series function cannot be solved without a definite functional expression

it has rarely been used in engineering. We developed a Taylor series using our proposed dendrite net (DD)

constructed a relation spectrum

and applied it to decomposition and analysis of models and systems. Specifically

knowledge of the intuitive link between muscle activity and 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 the 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 focused on muscle synergy and muscle coupling for the hand. The main contributions are twofold: (1) The findings concerning the hand contribute to the design of prosthetic hands; (2) The relation spectrum makes the online model human-readable

which unifies online performance and offline results. Code is available at https://github.com/liugang1234567/Gang-neuron.

关键词

泰勒级数关系谱树突网络假肢手机器学习工程

Keywords

Taylor seriesRelation spectrumDendrite net (DD)Prosthetic handsMachine learningEngineering

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