%0 Conference Paper
%1 magnussen2024optical
%A Magnussen, Birk Martin
%A Möckel, Frank
%A Jessulat, Maik
%A Stern, Claudius
%A Sick, Bernhard
%B International Conference on Bioinformatics and Computational Biology (ICBCB)
%D 2024
%I IEEE
%K imported itegpub isac-www reflection_spectroscopy neural_networks continuous_feature_networks body_mass_index
%T Optical Detection of the Body Mass Index and Related Parameters Using Multiple Spatially Resolved Reflection Spectroscopy
%X Obesity is a common problem for many people. In order to assist people combating obesity, providing methods to quickly, easily, and inexpensively assess their body composition is important.

This article investigates how noninvasive, optical sensors based on multiple spatially resolved reflection spectroscopy can be used to measure the body mass index and body composition parameters.
Using machine learning to train continuous feature networks, it is possible to predict the body mass index of a subject, with a correlation of $R=0.61$ with $p<0.0001$. Similarly, the predicted body mass index shows correlations to both the subject's visceral fat ($R=0.44,p=0.0023$) and skeletal muscle mass index ($R=0.52,p=0.0003$), indicating that the trained neural network is capable of identifying both types of tissue. Strategies to independently detect either type of tissue are discussed.