Journal of System Simulation
Abstract
Abstract: Invasive blood glucose measurement has a strong sense of discomfort and risk of infection, so the study of non-invasive blood glucose has a strong practical significance. At present, the optical method is not convenient for practical use, and the energy conservation method requires strict requirements. In view of the above problems, infrared thermography is used to detect blood glucose. After acquiring infrared thermal images of face figure, we extract the gray feature and reduce its dimension. In order to speed up the training and prevent over fitting, depth regression network is improved to model the infrared thermal image gray feature, and the ideal testing results have been achieved in the test set, which provides a new method of research and design for the noninvasive blood glucose detection algorithm research.
Recommended Citation
He, Mengjia; Wu, Yingnian; and Rui, Yang
(2019)
"Research on Nondestructive Blood Glucose Cloud Detection System Based on Improved Deep Regression Network,"
Journal of System Simulation: Vol. 31:
Iss.
11, Article 36.
DOI: 10.16182/j.issn1004731x.joss.19-FZ0357
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol31/iss11/36
First Page
2492
Revised Date
2019-07-30
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.19-FZ0357
Last Page
2498
CLC
TP391.9
Recommended Citation
He Mengjia, Wu Yingnian, Yang Rui. Research on Nondestructive Blood Glucose Cloud Detection System Based on Improved Deep Regression Network[J]. Journal of System Simulation, 2019, 31(11): 2492-2498.
DOI
10.16182/j.issn1004731x.joss.19-FZ0357
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