KeR-EGI, a new index of gait quantification based on electromyography

Abstract

PurposeTo define a new index of gait pathology in adults based on electromyographic data: the Ker-EGI for Kerpape-Rennes EMG-based Gait Index. The principle is similar to the one of Gait Deviation Index but using EMG profiles instead of joint angles. It first needs to build a database of healthy subjects gait to be able then to quantify the deviation of one peculiar patient’s gait from this typical behavior. MethodsNinety adults (59 healthy and 31 pathological) participated to this study. All pathological subjects had a diagnosis of central nervous system disorder. On each subject we collected the joint angles and the activation profile of seven muscles of each lower limb. Moreover, we recorded two videos (face and profile) of each patient to compute his/her Edinburgh Visual Gait Score (EVGS). Then for each patient, we computed the GGI (Gillette Gait Index), the GDI (Gait Deviation Index) and the Ker-EGI. ResultsCorrelation Ker-EGI and each of the three kinematical indices (GGI, GDI, EVGS) is fair to good (respectively R2=0.62, 0.42, and 0.69). ConclusionKeR-EGI is a valid index to evaluate gait and is complementary to one of these three kinematical indices providing synthetic vision on patients’ motor control abilities.