Machine Learning-Powered Microwave Device for Local Body Composition Assessment

In this article, a standalone microwave device is evaluated for its ability to assess local body composition with the ultimate goal to assess muscle quality. Data have been collected from volunteers who were measured on their thigh using the microwave device and ultrasound. A machine learning algorithm with three stages is designed that utilizes the stacked nature of the tissues in the thigh to predict skin and fat thickness and the cross-sectional area (CSA) of the rectus femoris muscle. The input to the algorithm is the signal response from the microwave sensor and also the prediction from the previous layers. The ultrasound measurements are used as the ground-truth labels for each tissue to train the machine learning models. The measurements were performed with two sensors, where the usage of the combined data from both sensors produced the best results for fat and muscle, 0.57 and 0.63 in
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score, respectively. In the drop analysis, a step where a select proportion of the data is temporarily removed, the identified models showed increased scores with a larger amount of data available, which indicates that learning of the models improves with more data. Although the results are encouraging, more data are ultimately needed to further study the algorithm.