Fractal Analysis of Surface Electromyography (EMG) Signal for Identify Hand Movements Using Critical Exponent Analysis

Recent advances in non-linear analysis have led to understand the complexity and self-similarity of surface electromyography (sEMG) signal. This research paper examines usage of critical exponent analysis method (CEM), a fractal dimension (FD) estimator, to study properties of the sEMG signal and to use these properties to identify various kinds of hand movements for prosthesis control and human-machine interface. The sEMG signals were recorded from ten healthy subjects with seven hand movements and eight muscle positions. Mean values and coefficient of variations of the FDs for all the experiments show that there are larger variations between hand movement types but there is small variation within hand movement. It also shows that the FD related to the self-affine property for the sEMG signal extracted from different hand activities 1.944~2.667. These results have also been evaluated and displayed as a box plot and analysis-of-variance (

p

value). It demonstrates that the FD value is suitable for using as an EMG feature extraction to characterize the sEMG signals compared to the commonly and popular sEMG feature, i.e., root mean square (RMS). The results also indicate that the

p

values of the FDs for six muscle positions was less than 0.0001 while that of the RMS, a candidate feature, ranged between 0.0003-0.1195. The FD that is computed by the CEM can be applied to be used as a feature for different kinds of sEMG application.