@article{ref1,
title="Real-time short-term pedestrian trajectory prediction based on gait biomechanics",
journal="Sensors (Basel)",
year="2022",
author="González, Leticia and López, Antonio M. and Álvarez, Juan C. and Alvarez, Diego",
volume="22",
number="15",
pages="e5828-e5828",
abstract="The short-term prediction of a person's trajectory during normal walking becomes necessary in many environments shared by humans and robots. Physics-based approaches based on Newton's laws of motion seem best suited for short-term predictions, but the intrinsic properties of human walking conflict with the foundations of the basic kinematical models compromising their performance. In this paper, we propose a short-time prediction method based on gait biomechanics for real-time applications. This method relays on a single biomechanical variable, and it has a low computational burden, turning it into a feasible solution to implement in low-cost portable devices. We evaluate its performance from an experimental benchmark where several subjects walked steadily over straight and curved paths. With this approach, the results indicate a performance good enough to be applicable to a wide range of human-robot interaction applications.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22155828",
url="http://dx.doi.org/10.3390/s22155828"
}