Gender dependent EMGs of runners resolved by time/frequency and principal pattern analysis.

A promising approach for the analysis of surface electromyograms is to use wavelets to determine the spectral distribution of the signal intensity at any time. The authors have recently proposed using non-linearly scaled wavelets to obtain intensity patterns, which reflect the spectral distribution at any given time point. Further analysis of intensity-patterns is greatly facilitated by representing them as linear combinations of a base set of principal-patterns. The weight with which each principal-pattern contributes to the intensity-pattern can be represented on a set of orthogonal axes that span a previously introduced pattern space. The purpose of the present study was to show how to use pattern space to discriminate and classify male and female runners based on the electromyograms of five muscles of the limb. The results showed that there were significant gender specific differences, which allowed more than a 95% correct classification of the subjects as males or females. Classification was possible irrespective of the shod condition while running. Gender specific differences occurred at well-defined time periods during the movement. Common to both genders was that spectral changes did not parallel the changes in total signal intensity.