Comparison of step length and heading estimation methods for indoor environments

In this paper, a comparison of IMU-based step length and heading estimation methods for indoor environments is presented. We compared two step detection methods: detecting the stance phase in each stride and detecting negative peaks in the horizontal acceleration filtered signal. Likewise, three step length estimation methods were compared: Zero Velocity Update (ZUPT), step frequency-based and step length parameter-based. Finally, the comparison of two heading estimation methods is presented: Heuristic Heading Reduction (HHR) and Zero Angular Rate update (ZARU). The yaw obtained from the sensor was corrected and compared with both heading estimation methods. All methods were tested at high, normal and low walking frequencies. Both step detections methods showed a good performance at normal and high step frequencies. The step length estimation method that depend of a linear parameter presented an error of 0.3m in a route of 96m and was the one with lowest error. The yaw corrected presents the lowest error of 3° in turns of 90°.