Development of a Discrete Spectrometric NIR Reflectance Glucometer

Campbell, Jake; Holder-Pearson, Lui; Pretty, Christopher G.; Benton, Connor; Knopp, Jennifer L.; Chase, J. Geoffrey

doi:10.1016/j.ifacol.2020.12.388

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…It was observed that the data were normal and the variances of differences were equal; therefore, no correction was needed. Paired t-test was applied to the dataset where p < 0.05 is set 50 , 64 . It can be observed from Table 12 that v0.001, and df = 100 were obtained with t-value = 0.59 for the finger and 0.56 for the wrist sensor leading to the acceptance of ,where it was concluded that there was no difference between the reference and predicted blood glucose levels.…”

Section: Resultsmentioning

confidence: 99%

“…Capillary loops consisting of blood glucose molecules are present in the dermal layer of the skin at a 2.0 mm depth, which is easily penetrated by NIR sensors 48 . From the literature, glucose absorption peaks are found at 660 nm, 940 nm, 1550 nm, and 1650 nm, where the penetration depth of light is highest at 940 nm 28 , 32 , 49 , 50 . Below 700 nm and above 950 nm, the penetration of light is challenging owing to the strong absorption from hemoglobin and water molecules.…”

Section: Methodsmentioning

confidence: 99%

“…Predictive models are selected by comparing and analyzing various regression algorithms from the literature. From the profound literature that discussed on the rationale behind the selection of regression algorithms, it was reported that the performance of predictive models depends on the methodologies implemented, the dataset created 50 , 64 – 69 , the size and heterogeneity of the dataset 70 . Regression algorithms differ in their principles of operation, advantages, and limitations.…”

Section: Methodsmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Rajeswari,

Vijayakumar

2024

Sci Rep

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“…Healthcare inequities due to increased rationing of care require solutions which enable increased productivity without an increase in resources. A broadly interoperable insulin pump paired with an equally openly-accessible glucose monitor ( 40 ) would enable a low-cost, equitable artificial pancreas system (LEAPS). In addition to improved patient-led diabetes care, a LEAPS solution would also more timely and efficient clinician assistance through cloud-based processing for patient-level and system-wide analysis.…”

Section: Discussionmentioning

confidence: 99%