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- W4315777356 abstract "Non-invasive blood glucose measurement is a significant challenge in academia and industry. In this paper, we invent a non-invasive glucose monitoring system with Edge-AI for connected healthcare and help patients and normal people to achieve comfortable healthcare services. In this study, we use a self-made infrared experimental instrument with a peak wavenumber of 3448 $cm^{-1}$ (wavelength 2900 nm) to obtain the physical signal value and map the physical signal to the real blood glucose value with various predictive algorithm models. The transmittance signal and the reflectance signal are measured and analyzed separately. From the result, both the transmittance signal and the reflectance signal reach the performance expectation (R <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> =0.990, R <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> =0.984). Furthermore, Clarke Error Grid Analysis (EGA) is adopted to evaluate and present the results. The EGA results show that almost all predicted values are concentrated in region A and region B, which are clinically correct and uncritical decisions. It proves that our non-invasive blood glucose monitoring system with the convergence of smart devices and Edge-AI can provide good technical support to connected healthcare services." @default.
- W4315777356 created "2023-01-13" @default.
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- W4315777356 date "2022-12-04" @default.
- W4315777356 modified "2023-09-27" @default.
- W4315777356 title "Non-invasive Blood Glucose Measurement with Mid-infrared Signal by Machine Learning Schemes" @default.
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- W4315777356 doi "https://doi.org/10.1109/gcwkshps56602.2022.10008511" @default.
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