A Method to Extract Motion Velocities of Limb and Trunk in Human Locomotion

Human motion monitoring is important in applications of movies, animation, sport training, physical rehabilitation, and human-robot interaction. There is a demand for a simple and easy-to-use method to recognize motions of limbs and trunk. In this paper, we developed a wearable velocity tracking device using two orthogonally-placed micro flow sensors to implement three-dimensional motion velocity measurement. In addition, we proposed a functional link artificial neural network (FLANN) model to extract trunk velocity and relative limb velocity from an absolute limb motion detected by the wearable tracking device according to their different dynamic features. Experiments were conducted to validate the effectiveness of the velocity tracking methodology. Results showed the proposed method with wearable device enables real-time measurements of motion velocities of limb and trunk, which were free of accumulated error, robust for dynamic walking and running, and simple to use.