Human Abnormal Gait Modeling via Hidden Markov Model

This paper presents a method for modeling human abnormal gait using hidden Markov model under the framework of a shoe-integrated system. The intelligent system focuses on modeling the following patterns: normal gait, toe in and toe out gait abnormalities. In the developed prototype, an inertial measurement unit (IMU) consisting of three-dimensional gyroscopes and accelerometers is employed to measure angular velocities and accelerations of human foot. Four force sensing resistors (FSRs) and one bend sensor are arranged on a insole of each foot for force and flexion information acquisition. The proposed method is mainly based on principal component analysis (PCA) for feature generation and hidden Markov model (HMM) for multi-pattern modeling. The "similarity distance measure" criterion is introduced to do model-to- model evaluation. Experiment results demonstrate the proposed approach is robust and efficient in detecting abnormal gait patterns. Our goal is to provide a cost-effective system for detecting gait abnormalities in order to assist persons with abnormal gaits in developing the normal walking pattern in their daily life.