Measuring complexity in different muscles during sustained contraction using fractal properties of SEMG signal

Modelling and analysis of surface Electromyogram (sEMG) signal has gained increasing attention in bio-signal processing for medical and healthcare applications. This research reports the study to examine the complexity in surface electromyogram signal measured from different muscles to identify the properties of muscles. Experiments were conducted to study the properties of the four muscle groups representing four sizes in length and complexities: Zygomaticus (facial), biceps, quadriceps and flexor digitorum superficialis (FDS). Complexity of the sEMG signal was computed using Higuchi's Fractal dimension. The relationship between FD and the muscle properties was investigated. Experimental results demonstrate that for a small variation in muscle contraction, there is very small change in the value of complexity (measured using Fractal dimension $\sim 0.1$%) and indicates that the larger and more complex muscles having a higher complexity at MVC. It is observed that the change in FD with muscle contraction is a result of changes in the properties of the particular muscle and its associated movement or change in length.