Accurate Identification of Gait Asymmetry in LLD Based on Wearable Sensors and Deep Learning

The relationship between lower limb length discrepancy (LLD) and gait symmetry in non-clinical environments has consistently garnered significant attention in the relevant research field. Accurate and objective quantification of human gait symmetry has remained a challenging task in recent studies. In this research, we propose a novel method that utilizes deep learning and wearable sensors to precisely measure the dynamic variations in LLD gait symmetry. In this study, we have developed an Att-CNN-LSTM hybrid deep learning model, which extracts spatiotemporal features closely associated with LLD gait symmetry from gait data measured by wearable sensors attached to the ankle, knee, and hip joints of the lower limbs. This allows for accurate differentiation between the left and right lower limbs of various levels of LLD. Our approach offers a new perspective for the early identification of LLD gait symmetry and presents a fresh solution for long-term outdoor monitoring of LLD gait symmetry. Experimental results demonstrate that the developed model can accurately identify LLD asymmetrical gaits at three different degrees , mild, moderate, and severe, with recognition accuracies exceeding 95% across all levels. Specifically, the recognition accuracy for severe LLD asymmetry gait can reach 97.89%. Additionally, for normal gait, our model achieves approximately 50% accuracy in gait symmetry identification.