ABNORMAL GAIT DETECTION USING INTELLIGENT SHOES

Abstract

This paper presents a method for modeling abnormal human 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 abnormality, and toe out abnormality. In the developed prototype, an inertial measurement unit (IMU) consisting of three-dimensional gyroscopes and accelerometers is employed to measure the angular velocities and accelerations of the foot. Four force sensing resistors (FSRs) and one bend sensor are arranged on the insole of each foot for force and flexion information acquisition. The proposed method is mainly based on supervised Principal Component Analysis (SPCA) 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. Experimental 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 a normal walking pattern in their daily life.