A new inertial sensor-based gait recognition method via deterministic learning

This paper presents a new gait recognition method based on acceleration and angular velocity data captured by inertial sensors via deterministic learning. These gait features describe the motion trajectories of human gait and contain rich information for persons identification. The gait recognition approach consists of two phases: a training phase and a recognition phase. In the training phase, the gait dynamics underlying different individuals' gaits are represented by the acceleration and angular velocity features, and are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In the recognition phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of human gait dynamics represented by the constant RBF networks are embedded in the estimators. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated. The average L1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. The test gait pattern similar to one of the training gait patterns can be recognized according to the smallest error principle. Finally, comprehensive experiments are carried out on the OU-ISIR biometric gait database: inertial sensor dataset, which includes at most 744 subjects (389 males and 355 females) and is now the world's largest inertial sensor-based gait database, to demonstrate the recognition performance of the proposed algorithm.