2026-04-14T21:05:22Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/821032026-01-27T09:00:12Zcom_10324_1167com_10324_931com_10324_894col_10324_1238

Pelaez Rodríguez, César Magdaleno González, Álvaro García Terán, José María Pérez-Aracil, Jorge Salcedo-Sanz, Sancho Lorenzana Ibán, Antolín 2026-01-23T19:54:05Z 2026-01-23T19:54:05Z 2024 Heliyon, Junio, 2025 2405-8440 https://uvadoc.uva.es/handle/10324/82103 10.1016/j.heliyon.2024.e32858 e32858 12 Heliyon 10 Producción Científica Human-induced force analysis plays an important role across a wide range of disciplines, including biomechanics, sport engineering, health monitoring or structural engineering. Specifically, this paper focuses on the replication of ground reaction forces (GRF) generated by humans during movement. They can provide critical information about human-mechanics and be used to optimize athletic performance, prevent and rehabilitate injuries and assess structural vibrations in engineering applications. It is presented an experimental approach that uses an electrodynamic shaker (APS 400) to replicate GRFs generated by humans during movement, with a high degree of accuracy. Successful force reconstruction implies a high fidelity in signal reproduction with the electrodynamic shaker, which leads to an inverse problem, where a reference signal must be replicated with a nonlinear and non-invertible system. The solution presented in this paper relies on the development of an iterative neural network and an inversion-free approach, which aims to generate the most effective drive signal that minimizes the error between the experimental force signal exerted by the shaker and the reference. After the optimization process, the weights of the neural network are updated to make the shaker behave as desired, achieving excellent results in both time and frequency domains. Los autores desean expresar su agradecimiento a la AEI, Gobierno de España (10.13039/501100011033) y a “FEDER Una manera de hacer Europa”, por el apoyo parcial a través de la subvención PID2022-140117NB-I00. Esta investigación también ha sido apoyada parcialmente por el proyecto PID2020-115454GB-C21 del Ministerio de Ciencia e Innovación de España (MICINN). application/pdf eng Elsevier Ltd. info:eu-repo/semantics/openAccess Forces reconstruction Human-induced forces Artificial neural networks Electrodynamic shaker Ground reaction forces An iterative neural network approach applied to human-induced force reconstruction using a non-linear electrodynamic shaker info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion SI