A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Blood glucose level needs to be monitored regularly to manage the health condition of hyperglycemic patients. The current glucose measurement approaches still rely on invasive techniques which are uncomfortable and raise the risk of infection. To facilitate daily care at home, in this article, we propose an intelligent, noninvasive blood glucose monitoring system which can differentiate a user's blood glucose level into normal, borderline, and warning based on smartphone photoplethysmography (PPG) signals. The main implementation processes of the proposed system include 1) a novel algorithm for acquiring PPG signals using only smartphone camera videos; 2) a fitting-based sliding window algorithm to remove varying degrees of baseline drifts and segment the signal into single periods; 3) extracting characteristic features from the Gaussian functions by comparing PPG signals at different blood glucose levels; 4) categorizing the valid samples into three glucose levels by applying machine learning algorithms. Our proposed system was evaluated on a data set of 80 subjects. Experimental results demonstrate that the system can separate valid signals from invalid ones at an accuracy of 97.54% and the overall accuracy of estimating the blood glucose levels reaches 81.49%. The proposed system provides a reference for the introduction of noninvasive blood glucose technology into daily or clinical applications. This article also indicates that smartphone-based PPG signals have great potential to assess an individual's blood glucose level.
Date Issued
2020-11-01
Date Acceptance
2020-02-14
Citation
IEEE Transactions on Industrial Informatics, 2020, 16 (11), pp.7209-7218
ISSN
1551-3203
Publisher
Institute of Electrical and Electronics Engineers
Start Page
7209
End Page
7218
Journal / Book Title
IEEE Transactions on Industrial Informatics
Volume
16
Issue
11
Copyright Statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000554904700047&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Technology
Automation & Control Systems
Computer Science, Interdisciplinary Applications
Engineering, Industrial
Computer Science
Engineering
Daily care
gaussian fitting
healthcare based on machine learning
noninvasive blood glucose monitoring
smartphone photoplethysmography (PPG) signal
PHOTOPLETHYSMOGRAPH
PRESSURE
WRIST
Publication Status
Published
Date Publish Online
2020-02-20