Next Article in Journal
Simultaneous/Selective Detection of Dopamine and Ascorbic Acid at Synthetic Zeolite-Modified/Graphite-Epoxy Composite Macro/Quasi-Microelectrodes
Previous Article in Journal
An Integral Model for Target Tracking Based on the Use of a WSN
Article Menu

Export Article

Open AccessArticle
Sensors 2013, 13(6), 7279-7295;

Non-Invasive Continuous Glucose Monitoring with Multi-Sensor Systems: A Monte Carlo-Based Methodology for Assessing Calibration Robustness

Department of Information Engineering, University of Padova, via Gradenigo 6B, Padova 35131, Italy
Biovotion AG, Technoparkstrasse 1, Zurich 8005, Switzerland
Author to whom correspondence should be addressed.
Received: 2 April 2013 / Revised: 25 April 2013 / Accepted: 22 May 2013 / Published: 3 June 2013
(This article belongs to the Section Biosensors)
Full-Text   |   PDF [613 KB, uploaded 21 June 2014]


In diabetes research, non-invasive continuous glucose monitoring (NI-CGM) devices represent a new and appealing frontier. In the last years, some multi-sensor devices for NI-CGM have been proposed, which exploit several sensors measuring phenomena of different nature, not only for measuring glucose related signals, but also signals reflecting some possible perturbing processes (temperature, blood perfusion). Estimation of glucose levels is then obtained combining these signals through a mathematical model which requires an initial calibration step exploiting one reference blood glucose (RBG) sample. Even if promising results have been obtained, especially in hospitalized volunteers, at present the temporal accuracy of NI-CGM sensors may suffer because of environmental and physiological interferences. The aim of this work is to develop a general methodology, based on Monte Carlo (MC) simulation, to assess the robustness of the calibration step used by NI-CGM devices against these disturbances. The proposed methodology is illustrated considering two examples: the first concerns the possible detrimental influence of sweat events, while the second deals with calibration scheduling. For implementing both examples, 45 datasets collected by the Solianis Multisensor system are considered. In the first example, the MC methodology suggests that no further calibration adjustments are needed after the occurrence of sweat events, because the “Multisensor+model” system is able to deal with the disturbance. The second case study shows how to identify the best time interval to update the model’s calibration for improving the accuracy of the estimated glucose. The methodology proposed in this work is of general applicability and can be helpful in making those incremental steps in NI-CGM devices development needed to further improve their performance. View Full-Text
Keywords: diabetes; model; multisensor diabetes; model; multisensor
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Zanon, M.; Sparacino, G.; Facchinetti, A.; Talary, M.S.; Mueller, M.; Caduff, A.; Cobelli, C. Non-Invasive Continuous Glucose Monitoring with Multi-Sensor Systems: A Monte Carlo-Based Methodology for Assessing Calibration Robustness. Sensors 2013, 13, 7279-7295.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top