Simplifying Hill-based muscle models through generalized extensible fuzzy heuristic implementation

Amy J. O'Brien

doi:10.1117/12.670881

17 April 2006 Simplifying Hill-based muscle models through generalized extensible fuzzy heuristic implementation

Amy J. O'Brien

Proceedings Volume 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV; 62470Q (2006) https://doi.org/10.1117/12.670881
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

Amy J. O'Brien "Simplifying Hill-based muscle models through generalized extensible fuzzy heuristic implementation", Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470Q (17 April 2006); https://doi.org/10.1117/12.670881

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KEYWORDS

Fuzzy logic

Systems modeling

Mathematical modeling

Electromyography

Chemical elements

Data modeling

Fuzzy systems

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