A Smartphone Inertial Sensor Based Recursive Zero-Velocity Detection Approach Yizhen Wang 1 , Xiangyi Meng 1 , Rui Xu 1 , Xuantong Chen 1 , Lingxiang Zheng 1 , Biyu Tang 1(B) , Ao Peng 1 , Lulu Yuan 1 , Qi Yang 1 , Haibin Shi 1 , Xiaoyang Ruan 1 , and Huiru Zheng 2 1 School of Information Science and Engineering, Xiamen University, Xiamen, China {lxzheng,tby}@xmu.edu.cn 2 School of Computing and Mathematics, University of Ulster, Newtownabbey, UK h.zheng@ulster.ac.uk Abstract. A reliable and robust zero velocity points (ZVPs) detection approach is important to restrain the accumulative error in the pedes- trian inertial navigation systems. A novel recursive zero-velocity detec- tion (RZVD) approach for smartphone based pedestrian dead reckoning systems is proposed in this paper. It combined the adaptive threshold and context information of the vertical velocity to verify the correct- ness of ZVP detection and fixed the incorrect ZVPs recursively. The test results show that the performance of the proposed approach is better than original method. It indicates that the proposed approach is help- ful to eliminate the serious estimation error caused by false detection of ZVPs. Keywords: ZUPT · Smartphone · Pedestrian dead reckoning system Recursive zero-velocity detection 1 Introduction Zero-velocity-update (ZUPT) is widely used in the inertial measurement unit (IMU) based pedestrian dead reckoning systems. The inertial sensors error is hard to eliminate, but the error growth of the IMU can be bounded during zero velocity period which reduces the accumulated error effectively. It means that a reliable ZUPT algorithm with few false detection of ZVPs is important for the error remove. To carry out the zero-velocity detection, some threshold values (for sen- sor values) must be assigned. Accelerometer and gyroscope are used wildly in zero-velocity (ZV) detection. In paper [5], the ZV detection is only based on accelerometer output which is good for normal walking scenarios, but not for both walking and running. In order to improve the robustness of ZV detec- tion, Paper [7] uses the threshold method that combines the module value of c  Springer Nature Singapore Pte Ltd. 2018 J. C. Hung et al. (Eds.): FC 2017, LNEE 464, pp. 162–168, 2018. https://doi.org/10.1007/978-981-10-7398-4_17