A Step Length Estimation Model of Coefficient Self-Determined Based on Peak-Valley Detection

Abstract EN

Without any preinstalled infrastructure, pedestrian dead reckoning (PDR) is a promising indoor positioning technology for pedestrians carrying portable devices to navigate.

Step detection and step length estimation (SLE) are two essential components for the pedestrian navigation based on PDR.

To solve the overcounting problem, this study proposes a peak-valley detection method, which can remove the abnormal values effectively.

The current step length models mostly depend on individual parameters that need to be predetermined for different users.

Based on fuzzy logic (FL), we establish a rule base that can adjust the coefficient in the Weinberg model adaptively for every detected step of various human shapes walking.

Specifically, to determine the FL rule base, we collect user acceleration data from 10 volunteers walking under the combination of diverse step length and stride frequency, and each one walks 49 times at all.

The experimental results demonstrate that our proposed method adapts to different kinds of persons walking at various step velocities.

Peak-valley detection can achieve an average accuracy of 99.77% during 500 steps of free walking.

Besides, the average errors of 5 testers are all less than 4 m per 100 m and the smallest one is 1.74 m per 100 m using our coefficient self-determined step length estimation model.