Hybrid Gait Recognition Method Based on Phase Trajectory

Gait recognition is critical to the activity monitoring, health management, assistance control of prostheses and exoskeletons, etc. This study aims to improve the gait classification performance on daily hybrid locomotions. We found the hip angle phase trajectories present significantly gait-dependent patterns, whereas the phase patterns show repeated limit cycles for periodic gaits while half-cycles or dots for aperiodic gaits. By converting the gait recognition issue into an image classification problem, we propose to use a convolution neural network (CNN) to learn the gait-dependent phase pattern images. Besides, to enhance the gait transition stability, we further integrate the prior state transition probability with category likelihood via dynamic Bayesian network. The proposed method has been experimented with 6 healthy subjects on standing, sitting, stand-to-sit, sit-to-stand, walking, running, stair ascending, and stair descending gaits. The overall leave-one-out cross-validation accuracy in continuous time is 96.15%.