Thanks for calling this out.
That was also my impression after having skimmed their paper: the only link to glucose monitoring is that the authors mention a few papers on the topic to motivate their research.
And looking at the papers they cite, I see little evidence that this approach could work in practice in the near future. Most of the citations [2, 15, 16] are to their own work, which did not look at glucose monitoring
in the human body.
This is not my field of expertise, and maybe I am misunderstanding the papers. But it seems that there is little evidence that non-invasive glucose monitoring via measuring dielectric properties works reliably in practice. No in-the-wild studies, no investigation of potentially confounding factors.
Take for example citation 22 from the paper. A study where the authors propose a new antenna design. They seem to measure how the pancreas changes size during insulin production by monitoring its dielectric properties. IIUC, they look for a dip in the frequency spectrum caused by absorption of a certain frequency band.
But their measurements show an even larger effect when measuring on the thumb instead of the pancreas. This effect is not explained at all. (My guess: after having patients fast for 8-10 hours, giving them glucose will have an effect on the whole metabolism, resulting in higher blood flow, and that's what they measured).
Also, while they operate the antenna in the GHz range, they use a cheap USB soundcard (sampling rate 44.1 kHz) for capturing the signal. I did not understand this at all. They also repeatedly use the term "dielectric radiation". Seems to be a rather uncommon term?
The "machine learning algorithms" mentioned in the title seem to be a simple linear regression?
They claim an accuracy of ~90% and show some sample results. The complete study data is only available upon request, however.
[22] S.J. Jebasingh Kirubakaran, M. Anto Bennet, N.R. Shanker,
Non-Invasive antenna sensor based continuous glucose monitoring using pancreas dielectric radiation signal energy levels and machine learning algorithms,
Biomedical Signal Processing and Control, Volume 85, 2023, 105072,
https://doi.org/10.1016/j.bspc.2023.105072