Highly sensitive blood glucose monitoring sensor with adjustable resonant frequency based on MP-CSRR

Wu, Wenqi; Xiao, Xia; Wang, Zengxiang; Sun, Junlong; Zhang, Xiaofeng

doi:10.1016/j.sna.2023.115004

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…However, the studies discussed are not executed in real time 11 , 15 , and a few of them are executed on blood samples 8 , 10 , 12 , 13 , 15 , which is a major limitation. Executing studies on real-time blood samples and comparing them with non-invasive sensors will allow us to understand whether any interference from biological differences in blood, susceptibility to interference from fats and proteins, and physiological conditions such as breathing, sweating, cardiac activity, and dehydration can alter the measurements due to changes in the permittivity and affect the sensitivity of the sensor 13 , 16 , 17 .…”

Section: Introductionmentioning

confidence: 99%

Development of sensor system and data analytic framework for non-invasive blood glucose prediction

Rajeswari,

Vijayakumar

2024

Sci Rep

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Periodic quantification of blood glucose levels is performed using painful, invasive methods. The proposed work presents the development of a noninvasive glucose-monitoring device with two sensors, i.e., finger and wrist bands. The sensor system was designed with a near-infrared (NIR) wavelength of 940 nm emitter and a 900–1700 nm detector. This study included 101 diabetic and non-diabetic volunteers. The obtained dataset was subjected to pre-processing, exploratory data analysis (EDA), data visualization, and integration methods. Ambiguities such as the effects of skin color, ambient light, and finger pressure on the sensor were overcome in the proposed ‘niGLUC-2.0v’. niGLUC-2.0v was validated with performance metrics where accuracy of 99.02%, mean absolute error (MAE) of 0.15, mean square error (MSE) of 0.22 for finger, and accuracy of 99.96%, MAE of 0.06, MSE of 0.006 for wrist prototype with ridge regression (RR) were achieved. Bland–Altman analysis was performed, where 98% of the data points were within ± 1.96 standard deviation (SD), 100% were under zone A of the Clarke Error Grid (CEG), and statistical analysis showed p < 0.05 on evaluated accuracy. Thus, niGLUC-2.0v is suitable in the medical and personal care fields for continuous real-time blood glucose monitoring.

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Section: Introductionmentioning

confidence: 99%

Development of sensor system and data analytic framework for non-invasive blood glucose prediction

Rajeswari,

Vijayakumar

2024

Sci Rep

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Detection of carcinoembryonic antigen specificity using microwave biosensor with machine learning

Lei,

Zhang,

Wang

et al. 2025

Biosensors and Bioelectronics

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Design of dual peak star shaped metamaterial absorber for S and C band sensing applications

Khalil,

Yong,

Islam

et al. 2024

Sci Rep

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This paper presents the detailed design configuration and investigation of a small-scale dual-band metamaterial absorber (MTMA) for solid and liquid sensing applications. The overall dimension of the MTMA unit cell is 10 × 10 × 1.57 mm 3 and constitutes an affordable FR-4 substrate. The absorber exhibits dual absorption peaks at 3.470 GHz for the S-band and 7.219 GHz for the C-band, respectively. Both absorption characteristics have been validated through comprehensive simulation and experimental procedures. The dual-band absorption rate exceeded 99% during simulations, and experimental validation showed an absorption rate above 98%. For sensing applications, various solid materials, including different Rogers substrates ( RT 5880, RT 5870, RT 4003 and RT 4835) and liquids such as sunflower and crown oil, were utilized. Our findings indicate that the proposed MTMA achieves a maximum Q-factor of 191 and a sensitivity of up to 2.5 for both solid and liquid sensing applications compared to previous studies. The simulation and experimental validation of the result indicate that suggested MTMA can be effectively used in different sensing applications such as the medical and communications industry.

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Highly sensitive blood glucose monitoring sensor with adjustable resonant frequency based on MP-CSRR

Cited by 3 publications

References 30 publications

Development of sensor system and data analytic framework for non-invasive blood glucose prediction

Development of sensor system and data analytic framework for non-invasive blood glucose prediction

Detection of carcinoembryonic antigen specificity using microwave biosensor with machine learning

Design of dual peak star shaped metamaterial absorber for S and C band sensing applications

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