An Optical Fiber-Based Data-Driven Method for Human Skin Temperature 3-D Mapping

Human skin temperature mapping provides abundant information of physiological conditions of human body, which provides supplementary or alternative indicators for disease monitoring or diagnosis. The existing models of temperature mapping or temperature field distribution of human skin are generally established by finite element method. Due to the complexity of biological systems, it is challenging to achieve high accuracy mathematical models of temperature field of human skin. The goal of this study is to establish human skin temperature three-dimensional (3-D) mapping platform by integrating optical fibers and improved genetic algorithm-back propagation (GA-BP) neural network. The proposed data-driven method is capable of acquiring entire human skin temperature 3-D mapping by simply measuring a few points on human skin. Multiple experiments were conducted to validate the proposed method on different areas of human skin in different ambient environments. In each experiment setting, the measured data and the model output data were compared. The mean absolute error in all the validation experiments is 0.11 °C, which is lower than that in the state of the art using physical modeling for skin temperature prediction and more close to clinical accuracy. The results show that the proposed approach is accurate and reliable, which may provide a platform technology for human skin temperature mapping that can be used in both medical and scientific studies as well as home monitoring.