Modeling of the interaction between human body and electromagnetic waves near resonance using machine learning

Human-Like digital models have been around for quite some time. They significantly contributed to the increase of the accuracy of the whole-body-average specific absorption rate estimations. However, the anatomical and morphological diversity between human beings has not yet been embraced by the actual anthropomorphic models for several reasons such as financial costs, excessive exposure of volunteers to electromagnetic waves, and the required number of technical experts needed to build one voxelized model. Recently, machine learning has been used to reduce the complexity of certain tasks. Yet, at least, having an anthropomorphic model per nation is still far away to achieve. To reduce the building cost of new human-like models, we build on the success of anthropomorphic models and machine learning to derive mathematical equations that make it possible to predict the Whole-body-average SAR from low frequencies up to twice the resonance frequency without any cost and excessive electromagnetic exposure of new volunteers. The completely new machine learning based equations are applicable for any age, ethnic group, and for both genders. They depend only on the human body’s morphological (height and weight) and anatomical parameters (tissue weights). In this work, we first address the whole-body-average SAR peak and we present a set of two estimators. In second, we show that the resonance frequency is not only a function of the height of the human body, to end up with a third estimation for the resonance frequency. These completely new estimators are finally combined into a novel function that links the whole-body-average SAR to the frequency. It shows the accurate prediction for low frequencies (10 MHz) up to twice the resonance frequency. The derived estimators for the maximum WBASAR and the resonance frequencies showed better results for low frequency exposure.