Generating vertical ground reaction forces using a stochastic data-driven model for pedestrian walking

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2025-04-30 DOI:10.1016/j.jocs.2025.102602

Alvaro Magdaleno , José María García-Terán , César Peláez-Rodríguez , Guillermo Fernández , Antolin Lorenzana

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引用次数: 0

Abstract

A novel time-domain approach to the characterization of the forces induced by a pedestrian is proposed. It focuses on the vertical component while walking, but thanks to how it is conceived, the algorithm can be easily adapted to other activities or any other force component. The work has been developed from the statistical point of view, so a stochastic data-driven model is finally obtained after the algorithm is applied to a set of experimentally measured steps. The model is composed of two mean vectors and their corresponding covariance matrices to represent the steps, as well as some more means and standard deviations to account for the step scaling and double support phase, under the assumption that the random variables follow normal distributions. Velocity and step length are also provided, so the model and the latter data enable the realistic generation of virtual gaits. Some application examples at different walking paces are shown, in which comparisons between the original steps and a set of virtual ones are performed to show the similarities between both. For reproducibility purposes, the data and the developed algorithm have been made available.

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利用随机数据驱动的行人行走模型产生垂直地面反作用力

提出了一种新的时域方法来表征行人引起的力。它专注于行走时的垂直分量，但由于它的构思方式，该算法可以很容易地适应其他活动或任何其他力分量。这项工作是从统计学的角度出发的，因此将该算法应用于一组实验测量的步长后，最终得到了一个随机数据驱动的模型。该模型在随机变量服从正态分布的假设下，由两个均值向量及其对应的协方差矩阵来表示步长，以及更多的均值和标准差来解释步长缩放和双支持阶段。同时还提供了速度和步长，使模型和后期数据能够真实地生成虚拟步态。给出了不同步速下的一些应用示例，并对原始步速和一组虚拟步速进行了比较，以显示两者之间的相似性。为了再现性的目的，已经提供了数据和开发的算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).