Estimating the heart rate (HR) response to exercises of a given intensity without the need of direct measurement is an open problem of great interest. We propose here a model that can estimate the heart rate response to exercise of constant intensity and its subsequent recovery, based on soft computing techniques. Multilayer perceptron artificial neural networks (NN) are implemented and trained using raw HR time series data. Our model’s input and output are the beat-to-beat time intervals and the HR values, respectively. The numerical results are very encouraging, as they indicate a mean relative square error of the estimated HR values of the order of 10⁻⁴ and an absolute error as low as 1.19 beats per minute, on average. Our model has also been proven to be superior when compared with existing mathematical models that predict HR values by numerical simulation. Our study concludes that our NN model can efficiently predict the HR response to any constant exercise intensity, a fact that can have many important applications, not only in the area of medicine and cardio-vascular health, but also in the areas of rehabilitation, general fitness, and competitive sport.

In this work, a machine learning technique employing a measurement-driven artificial neural network (ANN) architecture is proposed as a solution to the precise determination of the position and the moment of equivalent electric dipoles for unit characterization. These dipoles are used to match the generated electric field from various sources inside the spacecraft, during space exploration missions. Various methodologies for unit characterization have been proposed in the literature, the most common being the heuristic approaches, least squares variants, method of auxiliary sources, etc. Contrary to the previous time-consuming post-process methodologies, the proposed electric dipole neural network (EDMnet) can offer a real-time characterization of the measured unit (Device Under Test) after a proper training stage, especially as a fast pre-compliance method. The network uses the electric field vector, measured at 14 discrete locations, as input and reports the position and moment of the electric dipole that best matches the measured fields. In this work, various ANN architectures are tested and compared in order to select the optimal EDMnet parameters for accurate source identification. It is shown that the size of the artificial training data affects the performance of the network. The proposed EDMnet can provide accuracy in mm-scale, with respect to dipole positioning, greater than 99% in dipole moment prediction.