RT info:eu-repo/semantics/article
T1 An iterative neural network approach applied to human-induced force reconstruction using a non-linear electrodynamic shaker
A1 Peláez-Rodríguez, César
A1 Magdaleno, Álvaro
A1 García Terán, José María
A1 Pérez-Aracil, Jorge
A1 Salcedo-Sanz, Sancho
A1 Lorenzana, Antolín
K1 Forces reconstruction Human-induced forces Artificial neural networks Electrodynamic shaker Ground reaction forces
AB Human-induced force analysis plays an important role across a wide range of disciplines, includingbiomechanics, sport engineering, health monitoring or structural engineering. Specifically,this paper focuses on the replication of ground reaction forces (GRF) generated by humans duringmovement. They can provide critical information about human-mechanics and be used tooptimize athletic performance, prevent and rehabilitate injuries and assess structural vibrationsin engineering applications. It is presented an experimental approach that uses an electrodynamicshaker (APS 400) to replicate GRFs generated by humans during movement, with a high degreeof accuracy. Successful force reconstruction implies a high fidelity in signal reproduction withthe electrodynamic shaker, which leads to an inverse problem, where a reference signal must bereplicated with a nonlinear and non-invertible system. The solution presented in this paper relieson the development of an iterative neural network and an inversion-free approach, which aims togenerate the most effective drive signal that minimizes the error between the experimental forcesignal exerted by the shaker and the reference. After the optimization process, the weights of theneural network are updated to make the shaker behave as desired, achieving excellent results inboth time and frequency domains.
PB Elsevier Ltd.
SN 2405-8440
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/82103
UL https://uvadoc.uva.es/handle/10324/82103
LA eng
NO Heliyon, Junio, 2025
NO Producción Científica
DS UVaDOC
RD 23-ene-2026