Learning Gait Models With Varying Walking Speeds

Lower-limb exoskeletons can reduce the therapist's burden and quantify repetitive gait training for patients with gait impairments. For patient's gait training, different walking speeds are required at different rehabilitation stages. However, due to the uniqueness of gait patterns, it is challenging for lower limb exoskeletons to generate individualized gait patterns for patients with different anthropometric parameters. This letter proposed learning-based gait models to learn and reconstruct gait patterns from healthy subject's gait database, including the Gait Parameters Model (GPM) and the Gait Trajectory Model (GTM). The GPM employs Neural Networks to predict gait parameters with a given desired walking speed and the anthropometric parameters of the subject. The GTM utilizes Kernelized Movement Primitives (KMP) to reconstruct gait patterns with the predicted gait parameters. The proposed approach has been tested on a lower limb exoskeleton named AIDER. Experimental results indicate that the reconstructed gait patterns are very similar to the subject's actual gait patterns for varying walking speeds.