  We report a novel non-contact method for dehydration monitoring. We utilize a
transmit software defined radio (SDR) that impinges a wideband radio frequency
(RF) signal (of frequency 5.23 GHz) onto either the chest or the hand of a
subject who sits nearby. Further, another SDR in the closed vicinity collects
the RF signals reflected off the chest (or passed through the hand) of the
subject. Note that the two SDRs exchange orthogonal frequency division
multiplexing (OFDM) signal, whose individual subcarriers get modulated once it
reflects off (passes through) the chest (the hand) of the subject. This way,
the signal collected by the receive SDR consists of channel frequency response
(CFR) that captures the variation in the blood osmolality due to dehydration.
The received raw CFR data is then passed through a handful of machine learning
(ML) classifiers which once trained, output the classification result (i.e.,
whether a subject is hydrated or dehydrated). For the purpose of training our
ML classifiers, we have constructed our custom HCDDM-RF-5 dataset by collecting
data from 5 Muslim subjects (before and after sunset) who were fasting during
the month of Ramadan. Specifically, we have implemented and tested the
following ML classifiers (and their variants): K-nearest neighbour (KNN),
support vector machine (SVM), decision tree (DT), ensemble classifier, and
neural network classifier. Among all the classifiers, the neural network
classifier acheived the best classification accuracy, i.e., an accuracy of
93.8% for the proposed CBDM method, and an accuracy of 96.15% for the proposed
HBDM method. Compared to prior work where the reported accuracy is 97.83%, our
proposed non-contact method is slightly inferior (as we report a maximum
accuracy of 96.15%); nevertheless, the advantages of our non-contact
dehydration method speak for themselves.
