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首页> 外文会议>Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV >Simplifying Hill-based muscle models through generalized, extensible fuzzy heuristic implementation
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Simplifying Hill-based muscle models through generalized, extensible fuzzy heuristic implementation

机译：通过广义可扩展的模糊启发式实现简化基于Hill的肌肉模型

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Traditional dynamic muscle models based on work initially published by A. V. Hill in 1938 often rely on high-order systems of differential equations. While such models are very accurate and effective, they do not typically lend themselves to modification by clinicians who are unfamiliar with biomedical engineering and advanced mathematics. However, it is possible to develop a fuzzy heuristic implementation of a Hill-based model—the Fuzzy Logic Implemented Hill-based (FLIHI) muscle model—that offers several advantages over conventional state equation approaches. Because a fuzzy system is oriented by design to describe a model in linguistics rather than ordinary differential equation-based mathematics, the resulting fuzzy model can be more readily modified and extended by medical practitioners. It also stands to reason that a well-designed fuzzy inference system can be implemented with a degree of generalizability not often encountered in traditional state space models. Taking electromyogram (EMG) as one input to muscle, FLIHI is tantamount to a fuzzy EMG-to-muscle force estimator that captures dynamic muscle properties while providing robustness to partial or noisy data. One goal behind this approach is to encourage clinicians to rely on the model rather than assuming that muscle force as an output maps directly to smoothed EMG as an input. FLIHI's force estimate is more accurate than assuming force equal to smoothed EMG because FLIHI provides a transfer function that accounts for muscle's inherent nonlinearity. Furthermore, employing fuzzy logic should provide FLIHI with improved robustness over traditional mathematical approaches.
机译：基于A. V. Hill于1938年最初发表的工作的传统动态肌肉模型通常依赖于微分方程的高阶系统。尽管这样的模型非常准确和有效，但是它们通常不适合不熟悉生物医学工程和高级数学的临床医生修改。但是，有可能开发基于希尔的模型的模糊启发式实现（基于模糊逻辑的基于希尔的模型（FLIHI）的肌肉模型），与传统的状态方程方法相比，它具有多个优点。由于模糊系统的设计方向是用语言学描述模型，而不是基于普通的基于微分方程的数学模型，因此，医学工作者可以更轻松地修改和扩展所得的模糊模型。也有理由认为，可以以传统状态空间模型中不常遇到的一定程度的可推广性来实现设计良好的模糊推理系统。以肌电图（EMG）作为肌肉的一种输入，FLIHI等同于模糊的EMG肌力估算器，该估算器可以捕捉动态肌肉特性，同时为部分或嘈杂的数据提供鲁棒性。这种方法背后的一个目标是鼓励临床医生依赖模型，而不是假设肌肉力作为输出直接映射为平滑的EMG作为输入。 FLIHI的力估计比假设力等于平滑的EMG更准确，因为FLIHI提供了传递函数，可以解释肌肉固有的非线性。此外，与传统的数学方法相比，采用模糊逻辑应为FLIHI提供更高的鲁棒性。

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