Development of Smart Insulin Device for Non Invasive Blood Glucose Level Monitoring

2017 ◽  
Author(s):  
P. Sampath Reddy ◽  
K. Jyostna
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Non Invasive ◽  
Level Monitoring

DESIGN AND IMPLEMENTATION OF SMART INSULIN DEVICE FOR NON-INVASIVE BLOOD GLUCOSE LEVEL MONITORING

2019 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
V. DEEPTHI ◽  
R. MANISHA ◽  
YUNUS MOHAMMAD ◽  
P. BINDUSHREE ◽  
D. K. MANU ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Non Invasive ◽  
Design And Implementation ◽  
Level Monitoring

scholarly journals Comparison of Functional Regression and Functional Principal Component Regression for Estimating Non-Invasive Blood Glucose Level

2021 ◽  
Vol 5 (1) ◽  
pp. 14-25
Author(s):  
Nurul Fadhilah ◽  
Erfiani Erfiani ◽  
Indahwati Indahwati
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Principal Component Regression ◽  
Principal Component ◽  
Functional Regression ◽  
Glucose Levels ◽  
Non Invasive ◽  
Blood Glucose Levels ◽  
Functional Principal Component

The calibration method is an alternative method that can be used to analyze the relationship between invasive and non-invasive blood glucose levels. Calibration modeling generally has a large dimension and contains multicolinearities because usually in functional data the number of independent variables (p) is greater than the number of observations (p>n). Both problems can be overcome using Functional Regression (FR) and Functional Principal Component Regression (FPCR). FPCR is based on Principal Component Analysis (PCA). In FPCR, the data is transformed using a polynomial basis before data reduction. This research tried to model the equations of spectral calibration of voltage value excreted by non-invasive blood glucose level monitoring devices to predict blood glucose using FR and FPCR. This study aimed to determine the best calibration model for measuring non-invasive blood glucose levels with the FR and FPCR. The results of this research showed that the FR model had a bigger coefficient determination (R2) value and lower Root Mean Square Error (RMSE) and Root Mean Square Error Prediction (RMSEP) value than the FPCR model, which was 12.9%, 5.417, and 5.727 respectively. Overall, the calibration modeling with the FR model is the best model for estimate blood glucose level compared to the FPCR model.

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