iGLU 1.0: An Accurate Non-Invasive Near-Infrared Dual Short Wavelengths Spectroscopy based Glucometer for Smart Healthcare

@article{Jain2019iGLU1A,
  title={iGLU 1.0: An Accurate Non-Invasive Near-Infrared Dual Short Wavelengths Spectroscopy based Glucometer for Smart Healthcare},
  author={Prateek Jain and Amit Mahesh Joshi and Saraju P. Mohanty},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.04471}
}
In the case of diabetes, fingertip pricking for a blood sample is inconvenient for glucose measurement. Invasive approaches like laboratory test and one-touch glucometer enhance the risk of blood-related infections. To mitigate this important issue, in the current paper, we propose a novel Internet-of-Medical-Things (IoMT) enabled edge-device for precise, non-invasive blood glucose measurement. In this work, a near-infrared (NIR) spectroscopic technique using two wavelengths (940 nm, 1300 nm… 
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10 Citations
Background Citations
3
Methods Citations
1

10 Citations

iGLU 2.0: A New Wearable for Accurate Non-Invasive Continuous Serum Glucose Measurement in IoMT Framework
TLDR
A novel wearable non-invasive consumer device (called iGLU 2.0) which can be used by consumers for accurate continuous blood glucose monitoring and is incorporated with Internet-of-Medical-Things (IoMT) for smart healthcare.
iGLU 2.0: A new non-invasive, accurate serum glucometer for smart healthcare
TLDR
The first-ever non-invasive glucometer that takes into account serum glucose for high accuracy is presented, based on optical detection and precise machine learning (ML) regression models.
Everything You Wanted to Know About Noninvasive Glucose Measurement and Control
TLDR
There is requirement to develop the Internet-Medical-Things integrated Healthcare Cyber-Physical System (H-CPS) based Smart Healthcare framework for glucose measurement with purpose of continuous health monitoring.
Everything You Wanted to Know About Noninvasive Bloodless Glucose Monitoring
TLDR
To fulfill the imperatives for non-invasive blood glucose monitoring system, there is a need to configure an accurate measurement device that overcomes the problem of frequent pricking for blood samples.
iGLU 1.1: Towards a Glucose-Insulin Model based Closed Loop IoMT Framework for Automatic Insulin Control of Diabetic Patients
TLDR
The results represent that the proposed mathematical model may be useful to predict the glycaemic status of diabetic patients and personalise it as per their historical data.
Towards the Portability of a Capacitive-sensor based Non-invasive Glucometer: A Simulation Approach
TLDR
Proposed changes to improve the portability of a recently-reported capacitor-sensor based non- invasive glucometer include removal of some hardware such as the dummy capacitor and an OPAMP in one side and the generation of low-frequency, low-voltage ac signal from the microcontroller instead of using separate source in other side.
Deduction learning for precise noninvasive measurements of blood glucose with a dozen rounds of data for model training
Personalized modeling has long been anticipated to approach precise noninvasive blood glucose measurements, but challenged by limited data for training personal model and its unavoidable outlier…
90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c
TLDR
This work increased the number of subjects recruited to 2538 and found that the prediction accuracy reduced to undesirable 60.6%, but a personalized model could be a viable means of further investigation for NIBG prediction via deep learning regarding subjects with medication.
Internet of Things Framework for Device Integration in Automation Applications
Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis
TLDR
A novel approach was approached to remove the artifacts using Independent Components Analysis which boosted the performance and showed that the proposed approach would be useful to classify the upper limb movements accurately.

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