Multi-source information adaptive step detection method based on MEMS inertial measurement unit

In pedestrian dead reckoning (PDR) based on MEMS IMU, the detection for number of steps has a lower accuracy due to basing only on the accelerometer signals. To improve the detection accuracy, an adaptive detection method with multi-source information is proposed for the step detection. In this method, the adaptive detection is realized by comprehensively considering the angular velocity signals and the acceleration signals in the process of human movement and setting different adaptive threshold conditions based on different gait features. Unlike the conventional peak detection algorithm and the fixed-threshold detection algorithm, which have rather poor detection accuracy under abnormal human movements, the proposed method can accurately detect the number of steps under complex pedestrian motion behaviors. Experiment results show that the detection precisions by the proposed method can reach more than 98% under different pedestrian motion states.