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Eco Models of Distributed Systems Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Accuracy Improvement for Glucose Measurement in Handheld Devices by Using Neural Networks

Authors : Hieu Trung Huynh, Ho Dac Quan, Yonggwan Won

Published in: Future Data and Security Engineering

Publisher: Springer International Publishing

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Abstract

In this study, an approach for improving the accuracy of glucose measurement by using handheld devices is presented. The proposed approach is based on reducing the effects of hematocrit. The hematocrit is estimated by using a neural network which is trained by a non-iterative learning algorithm. The inputs for neural network are sampled from the transduced current curve. This current curve is generated during the chemical reactions of glucose measurement process in the handheld devices. The experiments performed on a real dataset show that the accuracy of glucose measurement using the handheld devices is improved by using the proposed approach.

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Metadata
Title
Accuracy Improvement for Glucose Measurement in Handheld Devices by Using Neural Networks
Authors
Hieu Trung Huynh
Ho Dac Quan
Yonggwan Won
Copyright Year
2017
Book
Future Data and Security Engineering

Print ISBN: 978-3-319-70003-8

Electronic ISBN: 978-3-319-70004-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-70004-5_21

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