Recovering Walking Trajectories from Local Measurements and Inertia Data

Lifeng Zhu, Chenghao Xu, Ke Shi, Wei Li, Aiguo Song and Xinhua Tang

Mathematical Problems in Engineering, 2020, vol. 2020, 1-11

Abstract:

Capturing walking trajectories is useful for motion planning and various location-based services. Traditionally, it is a challenging task because it is expensive to install infrastructures, especially in an outdoor setting. As another choice, the reconstructed walking trajectories suffer from the drifting problem from captured inertia data. In this work, we study the biped walking motion and propose a method to recover walking trajectories by introducing local measurements between the feet to the system, in combination with the orientation from inertia data. We design a few local measurements which can be passively captured. After analyzing these measurements, the walking trajectory is progressively recovered by solving a set of small-scale optimization problems. By comparing with the trajectories extracted from optical motion capture systems, we tested our method on different subjects, and the quality of the recovered walking trajectory is evaluated.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8825647

DOI: 10.1155/2020/8825647

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