Voltage intensity based non-invasive blood glucose monitoring

Menon, Krishnakumar N.; Hemachandran, Deepak; Kunnath, Abishek Thekkeyil

doi:10.1109/icccnt.2013.6726720

Cited by 19 publications

(4 citation statements)

References 5 publications

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“…Diabetes is a condition where the body does not produce enough insulin which causes high blood glucose levels. Uncontrolled diabetes can have negative effects and damage several organs and body tissues, including kidney failure, blindness and amputation [3].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there is a need to develop a non-invasive glucose monitoring device. There are many methods for creating a non-invasive glucose meter including optical technique ( [1], [3], [7], [8], [9], [10]), microwave technique [11], and electrochemical technique [12]. One of the famous optical techniques when designing a non-invasive glucose meter is by using the near-infrared (NIR) method.…”

Section: Introductionmentioning

confidence: 99%

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Design and Development of IoT Based Optical Glucometer

Hanafi,

Jumadi,

Abd Rahaman

2023

JEVA

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This research presents the development of an IoT based non-invasive glucometer using optical methods for diabetes monitoring. Diabetes needs to be identified as soon as possible and its development is closely monitored. One of the measures to control this disease is the daily monitoring of blood sugar levels by using glucometer. Glucometers on the market are invasive that require blood sampling or sensors implantation. To get a blood sample, it is necessary to prick a fingertip with a needle to obtain a blood sample. This procedure is uncomfortable, and repeated punctures increase the risk of transmission of infectious diseases. Alternatively, a non-invasive method using optical technique was proposed in this paper. The prototype device is mainly consisting of an NIR LED (940nm) acting as a light transmitter delivered through a finger and reflect to photodetector (BPW34) that acting as a light receiver. The prototype was integrated with the IoT platform using Arduino Cloud for monitoring purpose. The next step involved the development of calibration model. Ten healthy individuals were recruited to take part in glucose readings measurement that conducted by National Kidney Foundation Batu Pahat. A calibration model ( y=82.19x+12.91) was successfully obtained from this experiment. The accuracy of the developed device was between 93.2 and 96.9 % where the error percentage was found to be less than 7 %. In conclusion, a painless non-invasive glucometer based near-infrared LED and photodiode was successfully developed. For future development, the accuracy of the system can possibly be improved by using a longer wavelength of light emitter such as 1500 nm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Development of IoT Based Optical Glucometer

Hanafi,

Jumadi,

Abd Rahaman

2023

JEVA

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“…This technique includes a near infrared spectroscopy which can investigate variations in the depth of body tissues that differ from 1 to 100 mm or 10 to 50 micrometers through infrared wavelength transmissions [9][10][11][12]. The non-invasive blood glucose measuring devices have been carried out by monitoring blood flow and blood oxygen levels in a few seconds [13][14][15][16]. Detect changes in blood sugar concentration in vivo can be observed by spatial diffuse reflectance [17].…”

Section: Introductionmentioning

confidence: 99%

The Correlation of Blood Glucose Levels Measurement by Using Laser Speckle Imaging (LSI) Method

Zulkarnain¹

2019

JoTP

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Laser Speckle Imaging (LSI) method is the imaging of light interference events when it interacts on a rough surface. A laser beam which entered the surface of skin will experience reflection, refraction and scattering. Interaction between a laser beam with the surface of the skin when entering to membranes blood, provides contrast information that were resulted from the rays scattered in a certain position, recorded by a CMOS camera and translated by imageJ program. A picture will provide an information of the intensity from certain skin areas with dark light pattern which keeps blind due to the presence of blood flow that does not dwell. The result of contrast imaging was validated with direct measurement by blood glucose device. The objects were people with the age range of 17 until 63 years old. This study aims to find the correlation between contrast Speckle to blood sugar levels in the human body. The result revealed that correlation between contrast intensity of red, green and blue to glucose level are 0.3275; 0.3165; 0.3050 respectively, and then correlation between contrast intensity to age are 0.6410; 0.6554; 0.6526. The data indicated high correlation on contrast to age and average correlation on contrast to glucose blood level.

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“…By contrast, noninvasive blood glucose detection [ 5 – 7 ] offers a number of advantages, such as fast analysis speed, absence of trauma, low cost, and environmental friendliness. Noninvasive optical detection technology [ 8 – 10 ] is an important research topic in the area of noninvasive blood glucose detection. Since the 1970s, scientists have applied optics to determine the chemical composition of the human body.…”

Section: Introductionmentioning

confidence: 99%

Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection

2016

Computational and Mathematical Methods in Medicine

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Diabetes is a serious threat to human health. Thus, research on noninvasive blood glucose detection has become crucial locally and abroad. Near-infrared transmission spectroscopy has important applications in noninvasive glucose detection. Extracting useful information and selecting appropriate modeling methods can improve the robustness and accuracy of models for predicting blood glucose concentrations. Therefore, an improved signal reconstruction and calibration modeling method is proposed in this study. On the basis of improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and correlative coefficient, the sensitive intrinsic mode functions are selected to reconstruct spectroscopy signals for developing the calibration model using the support vector regression (SVR) method. The radial basis function kernel is selected for SVR, and three parameters, namely, insensitive loss coefficient ε, penalty parameter C, and width coefficient γ, are identified beforehand for the corresponding model. Particle swarm optimization (PSO) is employed to optimize the simultaneous selection of the three parameters. Results of the comparison experiments using PSO-SVR and partial least squares show that the proposed signal reconstitution method is feasible and can eliminate noise in spectroscopy signals. The prediction accuracy of model using PSO-SVR method is also found to be better than that of other methods for near-infrared noninvasive glucose detection.

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Voltage intensity based non-invasive blood glucose monitoring

Cited by 19 publications

References 5 publications

Design and Development of IoT Based Optical Glucometer

Design and Development of IoT Based Optical Glucometer

The Correlation of Blood Glucose Levels Measurement by Using Laser Speckle Imaging (LSI) Method

Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection

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