FUZZY INFERENCE SYSTEM FOR EMG ASYMMETRY INDEX VALIDATION

Human gait is not a periodic movement, its analysis will always present a typical intrasubject, intersubject variability. For this reason recent study enlighted the importance of taking in account a large number of gait cycles to be analyzed, making easier to compare parameters. From this point of view the detecting of gait deviations can be more accurate than visual assessment and can be used in different clinical cases. Although the existence of many protocols for recording gait signals, the selection of signal's features is the main issue when a machine learning algorithm is used to fit a designed model. In this study an organized data set composed by gait parameters observations, is processed through unsupervised and supervised learning algorithms in order to predict an asymmetry level assigned to each observation through the EMG asymmetry index proposed in a recent study. Each parameter used to build the dataset derives from sEMG signals recorded during trials performed in previous studies, in particular the electrical activity produced by four skeletal muscles of the subject's lower limbs, whom did not show any pathology that could affect walking task. In this study is possible to find the exploration of the input space and a design of a fuzzy logic controller by the tuning of fuzzy rules and membership function parameters.