Computation of fractal features based on the fractal analysis of surface Electromyogram to estimate force of contraction of different muscles

Sridhar Poosapadi Arjunan and Dinesh Kant Kumar

Computer Methods in Biomechanics and Biomedical Engineering, 2014, vol. 17, issue 3, 210-216

Abstract: This research study investigates the fractal properties of surface Electromyogram (sEMG) to estimate the force levels of contraction of three muscles with different cross-sectional areas (CSA): m. quadriceps–vastus lateralis, m. biceps brachii, andm. flexor digitorum superficialis. The fractal features were computed based on the fractal analysis of sEMG, signal recorded while performing sustained muscle contraction at different force levels. A comparison was performed between the fractal features and five other features reported in the literature. Linear regression analysis was carried out to determine the relationship between the force of contraction (20–100%) and features of sEMG. The results from the coefficients of regression (\[r ^{2}\] r2) show that the new fractal feature, maximum fractal length of the signal has highest correlation (range 0.88–0.90) when compared with other features which ranges from 0.34 to 0.74 for the three different muscles. This study suggests that the estimation of various levels of sustained contraction of muscles with varied CSA will provide a better insight into the biomechanics model that involves muscle properties and muscle activation.

Date: 2014
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DOI: 10.1080/10255842.2012.675055

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