AZUPT: Adaptive zero velocity update based on neural networks for pedestrian tracking

Zero Velocity Update (ZUPT) has played a key role in Pedestrian Dead Reckoning (PDR) with inertial measurement units (IMU). However, it is both crucial and difficult to determine ZUPT conditions given complex and varying motion types such as walking, fast walking or running, and different walking habits of distinct people, which have direct and significant impact on the tracking accuracy. In this research we proposed a model based on deep neural networks to determine moments when the ZUPT should be conducted. The proposed model ensures nearly identical performance regardless of different motion types. It has been demonstrated by extensive experiments conducted in three different scenarios that our model can work equally well with different pedestrians and walking patterns, enabling the wide use of PDR in real-world applications.