Gait analysis based on a hidden Markov model

For effective rehabilitation treatments, the status of a patient's gait needs to be analyzed precisely. Since the gait motions are cyclic with several gait phases, the gait motions can be analyzed by gait phases. In this paper, a hidden Markov model (HMM) is applied to analyze the gait phases in the gait motions. Smart Shoes are utilized to obtain the ground contact forces (GRFs) as observed data in the HMM. The posterior probabilities from the HMM are used to infer the gait phases. The proposed gait phase analysis methods are applied to actual gait data, and the results show that the proposed methods can be used to diagnose the status of a patient and evaluate a rehabilitation treatment.