Noncontact monitoring of dehydration using RF data collected off the chest and the hand

Buttar, H. M., Pervez, K., Ur Rahman, M. M., Mian, A. N., Riaz, K. and Abbasi, Q. H. (2024) Noncontact monitoring of dehydration using RF data collected off the chest and the hand. IEEE Sensors Journal, 24(3), pp. 3574-3582. (doi: 10.1109/JSEN.2023.3334590)

Abstract

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 reflected RF signals. 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 classify each subject as either hydrated or dehydrated. To train our ML classifiers, we have constructed our custom dataset by collecting data from 5 Muslim subjects who were fasting during the month of Ramadan. Specifically, we have implemented and tested the following ML classifiers: k-nearest neighbour, support vector machine, decision tree, ensemble classifier, and a neural network classifier. Among all the classifiers, the neural network classifier achieved the best classification accuracy, i.e., an accuracy of 93.8% (96.15%) for the proposed chest-based (hand-based) method. Compared to prior contact-based method where the reported accuracy is 97.83%, our proposed non-contact method provides slightly less accuracy than that of reported in the literature for contact-based method; nevertheless, the advantages of our non-contact dehydration method speak for themselves.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Abbasi, Professor Qammer
• Authors: Buttar, H. M., Pervez, K., Ur Rahman, M. M., Mian, A. N., Riaz, K., and Abbasi, Q. H.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: IEEE Sensors Journal
• Publisher: IEEE
• ISSN: 1530-437X
• ISSN (Online): 1558-1748
• Published Online: 28 November 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Sensors Journal 24(3):3574-3582
• Publisher Policy: Reproduced in accordance with the publisher copyright policy