Surface indicator of gait cycle variability based on Principal Component Analysis

Gait variability analysis has an important role in objective gait performance assessment. At the preferred speed, the gait stability increases, which is reflected in the reduced gait fluctuations. Decreasing and increasing walking speed affects the fluctuations to increase. In this paper, we have applied Principal Component Analysis (PCA) on foot kinetics and kinematics signals to extract a novel parameter for gait cycle variability assessment in the scenario of different walking speeds, without the need to observe sequential strides. We have proposed the area of the two-dimensional PCA cyclogram as the robust measure for gait variability. The results showed that the area of PCA cyclograms satisfied the expected quadratic dependence of walking speed as opposed to temporal and symmetry gait parameters, which have shown linear or no dependence.