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Proceedings Paper

Simplifying Hill-based muscle models through generalized extensible fuzzy heuristic implementation
Author(s): Amy J. O'Brien
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Paper Abstract

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.

Paper Details

Date Published: 17 April 2006
PDF: 15 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470Q (17 April 2006); doi: 10.1117/12.670881
Show Author Affiliations
Amy J. O'Brien, The George Washington Univ. (United States)
Strategic Analysis, Inc. (United States)


Published in SPIE Proceedings Vol. 6247:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Harold H. Szu, Editor(s)

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