Real-Time Human Foot Motion Localization Algorithm With Dynamic Speed

Abstract

One challenging problem for human–machine systems is to accurately estimate the position, velocity, and attitude of human foot motion, using an inertial measurement unit (IMU) sensor. This is particularly so in large environments affected by local magnetic disturbances. In this paper, we propose an algorithm that not only handles this problem, but also works efficiently in real time. The novelty of this paper lies mainly in two contributions: First, we propose a dynamic gait phase detection (GPD) method that can detect human foot gait phase with high accuracy (2.78% errors) in dynamic speeds of human foot motion, such as walking or running; second, we integrate an inertial navigation system, a GPD, a zero velocity update, and an extended Kalman filter in real time. The system can, thus, handle the IMU drift problem, as well as noise, for high-accuracy localization both indoors (0.375% errors) and outdoors (0.55% errors). To validate the proposed algorithm, we apply the motion-tracking system (MTS-ground truth), and the results show that 93.7% of the proposed algorithm's results converge on the MTS's results within a distance of less than 7.5 cm. Hence, the proposed algorithm can be embedded in wearable sensor devices for practical applications.