Dynamic muscles recruitment patterns using high-density EMG of diabetic patients in different severity degrees classified by a fuzzy system

Abstract

Many studies have described gait alterations in individuals with diabetic neuropathy that change the classic stereotyped normal gait. However, the knowledge of biomechanical causes oh this altered pattern remains insufficient. Besides that, previous studies that have investigated the gait biomechanics of diabetic patients had not distinguished the progressive degrees of the neuropathy in the studied groups. So, the identification of differences in gait alterations patterns between earlier and more advanced stages of the disease is not possible.The present study considers that the presence of neuropathy is a sign that the diabetic disease is advancing and that the progression of the neuropathy causes modifications on control strategies of gait that evolve with the disease and might be related to intrinsic biomechanical factors that affect the locomotor efficiency. The electromyographic patterns of diabetic individuals with neuropathy will be interpreted by the means of time, magnitude and frequency domain analysis (Wavelet Analysis) and complexity analysis (entropy). The conduction velocity, the main recruitment frequencies and energy will be identified in different instants of gait stance phase and during low intensity isometric contractions. Our theses are (i) the use of Fuzzy expert model will enable a better distinction among different stages of diabetic sensorimotor polyneuropathy, and differences in muscle activity could be elucidated with this classification; (ii) in most severe disease stages the differences in muscle activity will be accentuated. (AU)