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Updated: May 25, 2026
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Evaluating Postural Control and Lower-extremity Muscle Activation in Individuals with Chronic Ankle Instability
Published on: September 18, 2020
Evaluation of higher order statistics parameters for multi channel sEMG using different force levels.
Ganesh R Naik1, Dinesh K Kumar
1School of Electrical and Computer Engineering, RMIT University, Melbourne, Austrralia. ganesh.naik@rmit.edu.au
Summary
Surface Electromyogram (sEMG) signals are typically non-Gaussian, especially at higher contraction levels. However, at lower Maximum Voluntary Contractions (MVCs) below 30%, sEMG signals exhibit more Gaussian characteristics, as confirmed by Kurtosis analysis.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Signal Processing
Background:
- Electromyography (EMG) signals reflect muscle and nerve function.
- Signal morphology, including motor unit action potentials (MUAPs), is sensitive to electrode placement and muscle contraction intensity.
- Understanding sEMG signal characteristics is crucial for accurate biomechanical and clinical assessments.
Purpose of the Study:
- To evaluate the non-Gaussian nature of surface electromyogram (sEMG) signals.
- To investigate the influence of contraction levels on sEMG signal statistics.
- To apply higher-order statistics (HOS) for characterizing sEMG non-Gaussianity.
Main Methods:
- Experiments involved four distinct finger and wrist movements.
- Surface electromyogram (sEMG) data were collected at various Maximum Voluntary Contraction (MVC) levels.
- Higher-order statistics (HOS) parameters, specifically Kurtosis, were computed to assess signal non-Gaussianity.
Main Results:
- sEMG signals demonstrated non-Gaussian probability density functions (PDFs) under constant force and non-fatiguing conditions.
- For lower MVCs (below 30% MVC), sEMG signal PDFs tended towards a Gaussian distribution.
- Kurtosis values confirmed the observed trends in signal distribution across different MVC levels.
Conclusions:
- The non-Gaussianity of sEMG signals is dependent on the level of muscle contraction.
- Lower contraction intensities (below 30% MVC) result in sEMG signals that more closely resemble Gaussian processes.
- HOS parameters provide a quantitative method for assessing the statistical properties of sEMG signals in relation to muscle activation.
