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.
Highlights
As a typical motion of humans, walking is the most common way to locomote
We study the walking motion from the trajectories extracted from optical motion capture systems and design algorithms to reconstruct the walking trajectories from the designed measurements
We design experiments to validate our method in two phases
Summary
As a typical motion of humans, walking is the most common way to locomote. Capturing the walking trajectories is helpful for localizing the subject, synthesizing walking motions, or even understanding the human activities. A unified way to get the walking trajectories in both indoor or outdoor environments is to extract them from the data measured by inertial sensors. Advanced Kalman filters are proposed to integrate the information from inertia sensors, GNSS, and optical data. To further overcome their shortcomings, hybrid systems integrate multiple types of sensors and fuse different measurements [15,16,17]. Gait model was introduced as a prior to assist the motion capture from visual and inertia data in a limited space [21]. We propose to combine different sensory data in this task and show how they work in a large space As another class of solution, data-driven methods were reported to stabilize the captured motions. By preparing a database of captured motions, sparse accelerometer data are used to retrieve the closest motion [22, 23] as the reconstructed motion
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