A fuzzy scheme for gait cycle phase detection oriented to medical diagnosis

Abstract

Gait cycle phase detection provides useful information to diagnose possible problems on walking. The work reported here proposes the analysis of gait kinematic signals, extracted from videos, through fuzzy logic to automatically determine the different phases in the human gait cycle. The function of the fuzzy system is to detect the gait phases, loading response, mid-stance, terminal stance, pre-swing, initial swing, mid-swing, and terminal swing, using 2D information from a sagittal plane. The system was tested with normal and non-normal gait cycles. Experimental findings proved that the fuzzy detection system is able to correctly locate the phases using only 2D information. The maximum phase timing shift error generated was 2%. Thus, it may be concluded that the proposed system can be used to analyses gait kinematic and detect gait phases in normal cycle and absences of them in non-normal cycles. This information can be considered for gait anomaly detection and therapeutic purposes. © 2013 Springer-Verlag Berlin Heidelberg.