# iGLU 2.0: A new non-invasive, accurate serum glucometer for smart healthcare

arXiv: https://arxiv.org/abs/2001.09182 · HTML: https://mlsanity.com/paper/2001.09182/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2020.

## Built on by (most cited first, 6 of 6)

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- [Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare](https://mlsanity.com/paper/2111.11298/) (2021)
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- [SimplyMime: A Control at Our Fingertips](https://mlsanity.com/paper/2304.11377/) (2023)
- [Non-Invasive Glucose Prediction System Enhanced by Mixed Linear Models and Meta-Forests for Domain Generalization](https://mlsanity.com/paper/2409.07308/) (2024)

## Abstract (arXiv)

To best of the authors knowledge, this article presents the first-ever non-invasive glucometer that takes into account serum glucose for high accuracy. In case of blood glucose measurement, serum glucose value has always been considered precise blood glucose value during prandial modes. Serum glucose can be measured in laboratory and more stable glucose level compare to capillary glucose. However, this invasive approach is not convenient for frequent measurement. Sometimes, Conventional invasive blood glucose measurement may be responsible for cause of trauma and chance of blood related infections. To overcome this issue, in the current paper, we propose a novel Internet-of-Medical (IoMT) enabled glucometer for non-invasive precise serum glucose measurement. In this work, a near-infrared (NIR) spectroscopic technique has been used for glucose measurement. The novel device called iGLU 2.0 is based on optical detection and precise machine learning (ML) regression models. The optimal multiple polynomial regression and deep neural network models have been presented to analyze the precise measurement. The glucose values of serum are saved on cloud through open IoT platform for endocrinologist at remote location. To validate iGLU 2.0, Mean Absolute Relative Difference (mARD) and Average Error (AvgE) are obtained 6.07% and 6.09%, respectively from predicted blood glucose values for capillary glucose. For serum glucose, mARD and AvgE are found 4.86% and 4.88%, respectively. These results represent that the proposed non-invasive glucose measurement device is more precise for serum glucose compared to capillary glucose.