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Training a multivariable myoelectric mapping function to estimate fatigue.

Daniel R Rogers¹, Dawn T Macisaac

¹Department of Electrical and Computer Engineering, Institute of Biomedical Engineering, University of New Brunswick, Canada. dan.rogers@unb.ca

Journal of Electromyography and Kinesiology : Official Journal of the International Society of Electrophysiological Kinesiology

|December 8, 2009

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A new generalized mapping index (GMI) effectively assesses muscle fatigue using artificial neural networks (ANNs). This fatigue assessment method does not require subject-specific data, simplifying the process.

Area of Science:

Biomedical Engineering
Neuroscience
Sports Science

Background:

Muscle fatigue assessment is crucial for performance and injury prevention.
Existing myoelectric fatigue indices often require subject-specific calibration.
Artificial neural networks (ANNs) offer potential for advanced fatigue tracking.

Purpose of the Study:

To develop and validate a generalized mapping index (GMI) for muscle fatigue assessment.
To determine if GMI can perform as well as subject-specific mapping indices (MI).
To compare GMI and MI performance against traditional myoelectric fatigue measures.

Main Methods:

Surface myoelectric signals were recorded from nine participants during various fatiguing contractions.
ANNs were trained to create both subject-specific (MI) and generalized (GMI) fatigue indices.

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Performance was evaluated using a novel piece-wise linear signal-to-noise ratio.

Indices were compared to normalized spectral moments (NSM) and mean frequency (MF).

Main Results:

The generalized mapping index (GMI) demonstrated comparable performance to the subject-specific mapping index (MI).
GMI significantly outperformed traditional fatigue indices like normalized spectral moments (NSM) and mean frequency (MF).
The study confirmed that GMI effectively assesses fatigue without needing individual subject or contraction-specific data.

Conclusions:

A generalized mapping index (GMI) provides a robust and efficient method for muscle fatigue assessment.
GMI eliminates the need for extensive subject-specific baseline data collection, simplifying practical application.
This approach advances the field of non-invasive fatigue monitoring in various settings.