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Non-invasive Glucose Measurement Based on Ultrasonic Transducer and Near IR Spectrometer

  • Yanan Gao
  • Yukino Yamaoka
  • Yoshimitsu Nagao
  • Jiang Liu
  • Shigeru Shimamoto
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 513)

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

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

References

  1. 1.
    Yadav J, Rani A (2014) Near-infrared led based non-invasive blood glucose sensor. In Signal Processing and Integrated Networks (SPIN), pp 591–594Google Scholar
  2. 2.
    Amir O, Weinstein D (2007) Continuous noninvasive glucose monitoring technology based on occlusion spectroscopyCrossRefGoogle Scholar
  3. 3.
    Rinnan A, van den Berg F (2009) Review of the most common pre-processing techniques for near-infrared spectra. Trends Anal Chem 28(10):1201–1222CrossRefGoogle Scholar
  4. 4.
    Smilde A (2005) Multi-way analysis: applications in the chemical sciences. Wiley, HobokenGoogle Scholar
  5. 5.
    Cheng J-H (2017) PLSR applied to NIR and HSI spectral data modeling to predict chemical properties of fish muscle”. Food Eng Rev 9(1):36–49CrossRefGoogle Scholar
  6. 6.
    Malik BA, Naqash A (2016) Backpropagation artificial neural network for determination of glucose concentration from near-infrared spectra, ICACCI, IEEE, pp 2688–2691Google Scholar
  7. 7.
    Clarke WL (2005) The original clarke error grid analysis. Diab Technol Ther 7(5):776–779CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yanan Gao
    • 1
  • Yukino Yamaoka
    • 1
  • Yoshimitsu Nagao
    • 1
  • Jiang Liu
    • 1
  • Shigeru Shimamoto
    • 1
  1. 1.Waseda UniversityTokyoJapan

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