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.
