Non-invasive glucose measurement based on ultrasonic transducer and near IR spectrometer

Yanan Gao, Yukino Yamaoka, Yoshimitsu Nagao, Jiang Liu, Shigeru Shimamoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper studies a noninvasive method to measure glucose level based on ultrasonic transducer and near infrared spectrometer. A series pair data of ultrasonic transducer from human finger, palm, wrist and arm are collected six times a day, and 16 spectral data of NIR spectrometer (reflection) from finger are collected by an OGTT experiment. The collected data are calibrated by using partial least squares regression and feed-forward back-propagation artificial neural network to predict the glucose level. In this study, error grid analysis is used to validate the prediction performance. In addition, the accuracy of the calibration models is improved.

LanguageEnglish
Title of host publicationMobile and Wireless Technology 2018 - International Conference on Mobile and Wireless Technology ICMWT 2018
PublisherSpringer-Verlag
Pages33-40
Number of pages8
ISBN (Print)9789811310584
DOIs
Publication statusPublished - 2019 Jan 1
EventInternational Conference on Mobile and Wireless Technology, ICMWT 2018 - Kowloon, Hong Kong
Duration: 2018 Jun 252018 Jun 27

Publication series

NameLecture Notes in Electrical Engineering
Volume513
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Other

OtherInternational Conference on Mobile and Wireless Technology, ICMWT 2018
CountryHong Kong
CityKowloon
Period18/6/2518/6/27

Fingerprint

Infrared spectrometers
Ultrasonic transducers
Glucose
Backpropagation
Spectrometers
Calibration
Neural networks
Experiments

Keywords

  • BP-ANN
  • Glucose measurement
  • Near infrared spectrometer
  • Non-invasive
  • PLSR
  • Ultrasonic transducer

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Gao, Y., Yamaoka, Y., Nagao, Y., Liu, J., & Shimamoto, S. (2019). Non-invasive glucose measurement based on ultrasonic transducer and near IR spectrometer. In Mobile and Wireless Technology 2018 - International Conference on Mobile and Wireless Technology ICMWT 2018 (pp. 33-40). (Lecture Notes in Electrical Engineering; Vol. 513). Springer-Verlag. https://doi.org/10.1007/978-981-13-1059-1_3

Non-invasive glucose measurement based on ultrasonic transducer and near IR spectrometer. / Gao, Yanan; Yamaoka, Yukino; Nagao, Yoshimitsu; Liu, Jiang; Shimamoto, Shigeru.

Mobile and Wireless Technology 2018 - International Conference on Mobile and Wireless Technology ICMWT 2018. Springer-Verlag, 2019. p. 33-40 (Lecture Notes in Electrical Engineering; Vol. 513).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gao, Y, Yamaoka, Y, Nagao, Y, Liu, J & Shimamoto, S 2019, Non-invasive glucose measurement based on ultrasonic transducer and near IR spectrometer. in Mobile and Wireless Technology 2018 - International Conference on Mobile and Wireless Technology ICMWT 2018. Lecture Notes in Electrical Engineering, vol. 513, Springer-Verlag, pp. 33-40, International Conference on Mobile and Wireless Technology, ICMWT 2018, Kowloon, Hong Kong, 18/6/25. https://doi.org/10.1007/978-981-13-1059-1_3
Gao Y, Yamaoka Y, Nagao Y, Liu J, Shimamoto S. Non-invasive glucose measurement based on ultrasonic transducer and near IR spectrometer. In Mobile and Wireless Technology 2018 - International Conference on Mobile and Wireless Technology ICMWT 2018. Springer-Verlag. 2019. p. 33-40. (Lecture Notes in Electrical Engineering). https://doi.org/10.1007/978-981-13-1059-1_3
Gao, Yanan ; Yamaoka, Yukino ; Nagao, Yoshimitsu ; Liu, Jiang ; Shimamoto, Shigeru. / Non-invasive glucose measurement based on ultrasonic transducer and near IR spectrometer. Mobile and Wireless Technology 2018 - International Conference on Mobile and Wireless Technology ICMWT 2018. Springer-Verlag, 2019. pp. 33-40 (Lecture Notes in Electrical Engineering).
@inproceedings{1b1a98560a7543668d7b45957d727f07,
title = "Non-invasive glucose measurement based on ultrasonic transducer and near IR spectrometer",
abstract = "This paper studies a noninvasive method to measure glucose level based on ultrasonic transducer and near infrared spectrometer. A series pair data of ultrasonic transducer from human finger, palm, wrist and arm are collected six times a day, and 16 spectral data of NIR spectrometer (reflection) from finger are collected by an OGTT experiment. The collected data are calibrated by using partial least squares regression and feed-forward back-propagation artificial neural network to predict the glucose level. In this study, error grid analysis is used to validate the prediction performance. In addition, the accuracy of the calibration models is improved.",
keywords = "BP-ANN, Glucose measurement, Near infrared spectrometer, Non-invasive, PLSR, Ultrasonic transducer",
author = "Yanan Gao and Yukino Yamaoka and Yoshimitsu Nagao and Jiang Liu and Shigeru Shimamoto",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-981-13-1059-1_3",
language = "English",
isbn = "9789811310584",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer-Verlag",
pages = "33--40",
booktitle = "Mobile and Wireless Technology 2018 - International Conference on Mobile and Wireless Technology ICMWT 2018",

}

TY - GEN

T1 - Non-invasive glucose measurement based on ultrasonic transducer and near IR spectrometer

AU - Gao, Yanan

AU - Yamaoka, Yukino

AU - Nagao, Yoshimitsu

AU - Liu, Jiang

AU - Shimamoto, Shigeru

PY - 2019/1/1

Y1 - 2019/1/1

N2 - This paper studies a noninvasive method to measure glucose level based on ultrasonic transducer and near infrared spectrometer. A series pair data of ultrasonic transducer from human finger, palm, wrist and arm are collected six times a day, and 16 spectral data of NIR spectrometer (reflection) from finger are collected by an OGTT experiment. The collected data are calibrated by using partial least squares regression and feed-forward back-propagation artificial neural network to predict the glucose level. In this study, error grid analysis is used to validate the prediction performance. In addition, the accuracy of the calibration models is improved.

AB - This paper studies a noninvasive method to measure glucose level based on ultrasonic transducer and near infrared spectrometer. A series pair data of ultrasonic transducer from human finger, palm, wrist and arm are collected six times a day, and 16 spectral data of NIR spectrometer (reflection) from finger are collected by an OGTT experiment. The collected data are calibrated by using partial least squares regression and feed-forward back-propagation artificial neural network to predict the glucose level. In this study, error grid analysis is used to validate the prediction performance. In addition, the accuracy of the calibration models is improved.

KW - BP-ANN

KW - Glucose measurement

KW - Near infrared spectrometer

KW - Non-invasive

KW - PLSR

KW - Ultrasonic transducer

UR - http://www.scopus.com/inward/record.url?scp=85051125396&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051125396&partnerID=8YFLogxK

U2 - 10.1007/978-981-13-1059-1_3

DO - 10.1007/978-981-13-1059-1_3

M3 - Conference contribution

SN - 9789811310584

T3 - Lecture Notes in Electrical Engineering

SP - 33

EP - 40

BT - Mobile and Wireless Technology 2018 - International Conference on Mobile and Wireless Technology ICMWT 2018

PB - Springer-Verlag

ER -