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Noninvasive glucose monitoring using polarized light

Published:16 November 2020Publication History

ABSTRACT

We propose a compact noninvasive glucose monitoring system using polarized light, where a user simply needs to place her palm on the device for measuring her current glucose concentration level. The primary innovation of our system is the ability to minimize light scattering from the skin and extract weak changes in light polarization to estimate glucose concentration, all using low-cost hardware. Our system exploits multiple wavelengths and light intensity levels to mitigate the effect of user diversity and confounding factors (e.g., collagen and elastin in the dermis). It then infers glucose concentration using a generic learning model, thus no additional calibration is needed. We design and fabricate a compact (17 cm x 10 cm x 5 cm) and low-cost (i.e., <$250) prototype using off-the-shelf hardware. We evaluate our system with 41 diabetic patients and 9 healthy participants. In comparison to a continuous glucose monitor approved by U.S. Food and Drug Administration (FDA), 89% of our results are within zone A (clinically accurate) of the Clarke Error Grid. The absolute relative difference (ARD) is 10%. The r and p values of the Pearson correlation coefficients between our predicted glucose concentration and reference glucose concentration are 0.91 and 1.6 x 10-143, respectively. These errors are comparable with FDA-approved glucose sensors, which achieve ≈90% clinical accuracy with a 10% mean ARD.

References

  1. 2019. Diabetes Statistics. https://www.diabetesresearch.org/diabetes-statistics. (2019).Google ScholarGoogle Scholar
  2. 2019. FreeStyle Libre CGM sensors. https://www.freestylelibre.us/. (2019).Google ScholarGoogle Scholar
  3. 2019. Stereochemistry of Sugars: Diastereorners. http://www.chem.uiuc.edu/organic/. (2019).Google ScholarGoogle Scholar
  4. Simon Alaluf, Alan Heath, NIK Carter, Derek Atkins, Harish Mahalingam, Karen Barrett, RIA Kolb, and Nico Smit. 2001. Variation in melanin content and composition in type V and VI photoexposed and photoprotected human skin: the dominant role of DHI. Pigment Cell Research 14, 5 (2001), 337--347.Google ScholarGoogle ScholarCross RefCross Ref
  5. Alexandra Amaro-Ortiz, Betty Yan, and John A D'Orazio. 2014. Ultraviolet radiation, aging and the skin: prevention of damage by topical cAMP manipulation. Molecules 19, 5 (2014), 6202--6219.Google ScholarGoogle ScholarCross RefCross Ref
  6. Caerwyn Ash, Michael Dubec, Kelvin Donne, and Tim Bashford. 2017. Effect of wavelength and beam width on penetration in light-tissue interaction using computational methods. Lasers in medical science 32, 8 (2017), 1909--1918.Google ScholarGoogle Scholar
  7. Amay J Bandodkar, Wenzhao Jia, Ceren Yardimci, Xuan Wang, Julian Ramirez, and Joseph Wang. 2014. Tattoo-based noninvasive glucose monitoring: a proof-of-concept study. Analytical chemistry 87, 1 (2014), 394--398.Google ScholarGoogle Scholar
  8. Ananda Basu, Simmi Dube, Michael Slama, Isabel Errazuriz, Jose Carlos Amezcua, Yogish C Kudva, Thomas Peyser, Rickey E Carter, Claudio Cobelli, and Rita Basu. 2013. Time lag of glucose from intravascular to interstitial compartment in humans. Diabetes 62, 12 (2013), 4083--4087.Google ScholarGoogle ScholarCross RefCross Ref
  9. Alexander Bauer, Otto Hertzberg, Arne Küderle, Dominik Strobel, Miguel A Pleitez, and Werner Mäntele. 2018. IR-spectroscopy of skin in vivo: Optimal skin sites and properties for non-invasive glucose measurement by photoacoustic and photothermal spectroscopy. Journal of biophotonics 11, 1 (2018), e201600261.Google ScholarGoogle ScholarCross RefCross Ref
  10. Brent D Cameron and Gerard L Cote. 1997. Noninvasive glucose sensing utilizing a digital closed-loop polarimetric approach. IEEE Transactions on Biomedical Engineering 44, 12 (1997), 1221--1227.Google ScholarGoogle ScholarCross RefCross Ref
  11. Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM transactions on intelligent systems and technology (TIST) 2, 3 (2011), 1--27.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Jason Yuanzhe Chen, Qi Zhou, Gu Xu, Ryan Taoran Wang, Edward Guangqing Tai, Longhan Xie, Qianzhi Zhang, Yanyan Guan, and Xiaochun Huang. 2019. Non-invasive blood glucose measurement of 95% certainty by pressure regulated Mid-IR. Talanta 197 (2019), 211--217.Google ScholarGoogle ScholarCross RefCross Ref
  13. Tseng-Lin Chen, Yu-Lung Lo, Chia-Chi Liao, and Quoc-Hung Phan. 2018. Noninvasive measurement of glucose concentration on human fingertip by optical coherence tomography. Journal of biomedical optics 23, 4 (2018), 047001.Google ScholarGoogle ScholarCross RefCross Ref
  14. Zhen-cheng Chen, Xing-liang Jin, Jian-ming Zhu, Di-ya Wang, and Ting-ting Zhang. 2009. Non-invasive glucose measuring apparatus based on conservation of energy method. Journal of Central South University of Technology 16, 6 (2009), 982.Google ScholarGoogle ScholarCross RefCross Ref
  15. Ok Kyung Cho, Yoon Ok Kim, Hiroshi Mitsumaki, and Katsuhiko Kuwa. 2004. Noninvasive measurement of glucose by metabolic heat conformation method. Clinical chemistry 50, 10 (2004), 1894--1898.Google ScholarGoogle Scholar
  16. Soyun Cho, Mi Hee Shin, Yeon Kyung Kim, Jo-Eun Seo, Young Mee Lee, Chi-Hyun Park, and Jin Ho Chung. 2009. Effects of infrared radiation and heat on human skin aging in vivo. In Journal of Investigative Dermatology Symposium Proceedings, Vol. 14. Elsevier, 15--19.Google ScholarGoogle ScholarCross RefCross Ref
  17. Md Koushik Chowdhury, S Anuj, S Neeraj, and S Shiru. 2015. Error Grid Analysis of Reference and Predicted Blood Glucose Level Values as Obtained from The Normal and Prediabetic Human Volunteer. American Journal of Biomedical Engineering 5, 1 (2015), 6--14.Google ScholarGoogle Scholar
  18. Mark Christiansen, Timothy Bailey, Elaine Watkins, David Liljenquist, David Price, Katherine Nakamura, Robert Boock, and Thomas Peyser. 2013. A new-generation continuous glucose monitoring system: improved accuracy and reliability compared with a previous-generation system. Diabetes technology & therapeutics 15, 10 (2013), 881--888.Google ScholarGoogle Scholar
  19. Claudio Cobelli, Michele Schiavon, Chiara Dalla Man, Ananda Basu, and Rita Basu. 2016. Interstitial fluid glucose is not just a shifted-in-time but a distorted mirror of blood glucose: insight from an in silico study. Diabetes technology & therapeutics 18, 8 (2016), 505--511.Google ScholarGoogle Scholar
  20. Gerard L Cote. 2001. Noninvasive and minimally-invasive optical monitoring technologies. The Journal of nutrition 131, 5 (2001), 1596S--1604S.Google ScholarGoogle ScholarCross RefCross Ref
  21. Leszek Czupryniak, László Barkai, Svetlana Bolgarska, Agata Bronisz, Jan Broz, Katarzyna Cypryk, Marek Honka, Andrej Janez, Mladen Krnic, Nebojsa Lalic, and others. 2014. Self-monitoring of blood glucose in diabetes: from evidence to clinical reality in Central and Eastern Europe---recommendations from the international Central-Eastern European expert group. Diabetes technology & therapeutics 16, 7 (2014), 460--475.Google ScholarGoogle Scholar
  22. Carlos Eduardo Ferrante do Amaral and Benhard Wolf. 2008. Current development in non-invasive glucose monitoring. Medical engineering & physics 30, 5 (2008), 541--549.Google ScholarGoogle Scholar
  23. Annika MK Enejder, Thomas G Scecina, Jeankun Oh, Martin Hunter, WeiChuan Shih, Slobodan Sasic, Gary L Horowitz, and Michael S Feld. 2005. Raman spectroscopy for noninvasive glucose measurements. Journal of Biomedical Optics 10, 3 (2005), 031114.Google ScholarGoogle ScholarCross RefCross Ref
  24. Michael M Engelgau, KM Narayan, and William H Herman. 2000. Screening for type 2 diabetes. Diabetes care 23, 10 (2000), 1563--1580.Google ScholarGoogle ScholarCross RefCross Ref
  25. A Ergin, MJ Vilaboy, A Tchouassi, R Greene, and GA Thomas. 2003. Detection and analysis of glucose at metabolic concentration using Raman spectroscopy. In 2003 IEEE 29th Annual Proceedings of Bioengineering Conference. IEEE, 337--338.Google ScholarGoogle ScholarCross RefCross Ref
  26. Jerome H Friedman. 2002. Stochastic gradient boosting. Computational statistics & data analysis 38, 4 (2002), 367--378.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Nirmalya Ghosh, Michael FG Wood, Shu-hong Li, Richard D Weisel, Brian C Wilson, Ren-Ke Li, and I Alex Vitkin. 2009. Mueller matrix decomposition for polarized light assessment of biological tissues. Journal of biophotonics 2, 3 (2009), 145--156.Google ScholarGoogle ScholarCross RefCross Ref
  28. Willemijn Groenendaal, Golo Von Basum, Kristiane A Schmidt, Peter AJ Hilbers, and Natal AW van Riel. 2010. Quantifying the composition of human skin for glucose sensor development. (2010).Google ScholarGoogle Scholar
  29. Xinxin Guo, Michael FG Wood, Nirmalya Ghosh, and I Alex Vitkin. 2010. Depolarization of light in turbid media: a scattering event resolved Monte Carlo study. Applied optics 49, 2 (2010), 153--162.Google ScholarGoogle Scholar
  30. Steven L Jacques, Kenneth Lee, and Jessica C Ramella-Roman. 2000. Scattering of polarized light by biological tissues. In Saratov Fall Meeting'99: Optical Technologies in Biophysics and Medicine, Vol. 4001. International Society for Optics and Photonics, 14--28.Google ScholarGoogle ScholarCross RefCross Ref
  31. Jayoung Kim, Alan S Campbell, and Joseph Wang. 2018. Wearable non-invasive epidermal glucose sensors: A review. Talanta 177 (2018), 163--170.Google ScholarGoogle ScholarCross RefCross Ref
  32. DF Kimball, D Budker, DS English, C-H Li, A-T Nguyen, SM Rochester, A Sushkov, VV Yashchuk, and M Zolotorev. 2001. Progress towards fundamental symmetry tests with nonlinear optical rotation. In AIP Conference Proceedings, Vol. 596. AIP, 84--107.Google ScholarGoogle ScholarCross RefCross Ref
  33. Jae B Ko, Ok K Cho, Yoon O Kim, and Kazuo Yasuda. 2004. Body metabolism provides a foundation for noninvasive blood glucose monitoring. Diabetes care 27, 5 (2004), 1211--1212.Google ScholarGoogle ScholarCross RefCross Ref
  34. Paul AJ Kolarsick, Maria Ann Kolarsick, and Carolyn Goodwin. 2011. Anatomy and physiology of the skin. Journal of the Dermatology Nurses' Association 3, 4 (2011), 203--213.Google ScholarGoogle ScholarCross RefCross Ref
  35. Jonas Kottmann, Julien Rey, and Markus Sigrist. 2016. Mid-Infrared photoacoustic detection of glucose in human skin: towards non-invasive diagnostics. Sensors 16, 10 (2016), 1663.Google ScholarGoogle ScholarCross RefCross Ref
  36. Jonas Kottmann, Julien M Rey, Joachim Luginbühl, Ernst Reichmann, and Markus W Sigrist. 2012. Glucose sensing in human epidermis using mid-infrared photoacoustic detection. Biomedical optics express 3, 4 (2012), 667--680.Google ScholarGoogle Scholar
  37. Chak-hing Lam. 2009. Clinical evaluation of non-invasive blood glucose measurement by using near infrared spectroscopy via inter-and intra-subject analysis. Ph.D. Dissertation. The Hong Kong Polytechnic University.Google ScholarGoogle Scholar
  38. Sabbir Liakat, Kevin A Bors, Laura Xu, Callie M Woods, Jessica Doyle, and Claire F Gmachl. 2014. Noninvasive in vivo glucose sensing on human subjects using mid-infrared light. Biomedical optics express 5, 7 (2014), 2397--2404.Google ScholarGoogle Scholar
  39. Chia-Chi Liao and Yu-Lung Lo. 2015. Extraction of linear anisotropic parameters using optical coherence tomography and hybrid Mueller matrix formalism. Optics Express 23, 8 (2015), 10653--10667.Google ScholarGoogle ScholarCross RefCross Ref
  40. Tamar Lin, Avner Gal, Yulia Mayzel, Keren Horman, and Karnit Bahartan. 2017. Non-invasive glucose monitoring: a review of challenges and recent advances. Curr. Trends Biomed. Eng. Biosci 6 (2017), 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  41. Yu-Lung Lo and Tsung-Chih Yu. 2006. A polarimetric glucose sensor using a liquid-crystal polarization modulator driven by a sinusoidal signal. Optics communications 259, 1 (2006), 40--48.Google ScholarGoogle Scholar
  42. A Losoya-Leal, S Camacho-León, G Dieck-Assad, and SO Martínez-Chapa. 2012. State of the art and new perspectives in non-invasive glucose sensors. Revista Mexicana De Ingeniería Biomédica 33, 1 (2012), 41--52.Google ScholarGoogle Scholar
  43. Signe M Lundsgaard-Nielsen, Anders Pors, Stefan O Banke, Jan E Henriksen, Dietrich K Hepp, and Anders Weber. 2018. Critical-depth Raman spectroscopy enables home-use non-invasive glucose monitoring. PloS one 13, 5 (2018), e0197134.Google ScholarGoogle ScholarCross RefCross Ref
  44. Mon Arjay Malbog and Noel Linsangan. 2018. Non-invasive glucose meter for android-based devices. In Proceedings of the 2018 10th International Conference on Computer and Automation Engineering. 161--165.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Roger Mazze, Yariv Yogev, and Oded Langer. 2012. Measuring glucose exposure and variability using continuous glucose monitoring in normal and abnormal glucose metabolism in pregnancy. The Journal of Maternal-Fetal & Neonatal Medicine 25, 7 (2012), 1171--1175.Google ScholarGoogle ScholarCross RefCross Ref
  46. Roger J McNichols and Gerard L Cote. 2000. Optical glucose sensing in biological fluids: an overview. Journal of biomedical optics 5, 1 (2000), 5--17.Google ScholarGoogle ScholarCross RefCross Ref
  47. Mehrdad Mehdizadeh. 2010. Microwave/RF Applicators and Probes. (2010).Google ScholarGoogle Scholar
  48. Pradipta Mukherjee, Nathan Hagen, and Yukitoshi Otani. 2019. Glucose sensing in the presence of scattering by analyzing a partial Mueller matrix. Optik 180 (2019), 775--781.Google ScholarGoogle ScholarCross RefCross Ref
  49. Asmat Nawaz, Per Øhlckers, Steinar Sælid, Morten Jacobsen, and M Nadeem Akram. 2016. Non-invasive continuous blood glucose measurement techniques. Journal of Bioinformatics and Diabetes 1, 3 (2016), 01.Google ScholarGoogle ScholarCross RefCross Ref
  50. Praful P Pai, Pradyut Kumar Sanki, Arijit De, and Swapna Banerjee. 2015. NIR photoacoustic spectroscopy for non-invasive glucose measurement. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 7978--7981.Google ScholarGoogle ScholarCross RefCross Ref
  51. Quoc-Hung Phan, Tzu-Hsiang Jian, Yu-Ru Huang, You-Rui Lai, Wei-Zhe Xiao, and Shin-Wei Chen. 2020. Combination of surface plasmon resonance and differential Mueller matrix formalism for noninvasive glucose sensing. Optics and Lasers in Engineering 134 (2020), 106268.Google ScholarGoogle ScholarCross RefCross Ref
  52. Raju Poddar, Joseph Thomas Andrews, Pratyoosh Shukla, and Pratima Sen. 2008. Non-invasive glucose monitoring techniques: A review and current trends. arXiv preprint arXiv:0810.5755 (2008).Google ScholarGoogle Scholar
  53. Russell O Potts, Janet A. Tamada, and Michael J. Tierney. 2002. Glucose monitoring by reverse iontophoresis. Diabetes/metabolism research and reviews 18, S1 (2002), S49--S53.Google ScholarGoogle Scholar
  54. Georgeanne Purvinis, Brent D Cameron, and Douglas M Altrogge. 2011. Noninvasive polarimetric-based glucose monitoring: an in vivo study. Journal of diabetes science and technology 5, 2 (2011), 380--387.Google ScholarGoogle ScholarCross RefCross Ref
  55. Yun Qian, Yanchun Liang, Mu Li, Guoxiang Feng, and Xiaohu Shi. 2014. A resampling ensemble algorithm for classification of imbalance problems. Neuro-computing 143 (2014), 57--67.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Bernard Querleux, Thérèse Baldeweck, Stéphane Diridollou, Jean De Rigal, Etienne Huguet, Frédéric Leroy, and Victoria Holloway Barbosa. 2009. Skin from various ethnic origins and aging: an in vivo cross-sectional multimodality imaging study. Skin Research and Technology 15, 3 (2009), 306--313.Google ScholarGoogle ScholarCross RefCross Ref
  57. Anthony V Rawlings. 2006. Ethnic skin types: are there differences in skin structure and function? 1. International journal of cosmetic science 28, 2 (2006), 79--93.Google ScholarGoogle Scholar
  58. Florian Reiterer, Philipp Polterauer, Michael Schoemaker, Guenther Schmelzeisen-Redecker, Guido Freckmann, Lutz Heinemann, and Luigi Del Re. 2017. Significance and reliability of MARD for the accuracy of CGM systems. Journal of diabetes science and technology 11, 1 (2017), 59--67.Google ScholarGoogle ScholarCross RefCross Ref
  59. Zhong Ren, Guodong Liu, and Zhen Huang. 2014. Noninvasive detection of glucose level based on tunable pulsed laser induced photoacoustic technique. In International Symposium on Optoelectronic Technology and Application 2014: Laser and Optical Measurement Technology; and Fiber Optic Sensors, Vol. 9297. International Society for Optics and Photonics, 929709.Google ScholarGoogle Scholar
  60. David Rodbard. 2017. Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes. Diabetes technology & therapeutics 19, S3 (2017), S-25.Google ScholarGoogle Scholar
  61. Silonie Sachdeva and others. 2009. Fitzpatrick skin typing: Applications in dermatology. Indian Journal of Dermatology, Venereology, and Leprology 75, 1 (2009), 93.Google ScholarGoogle ScholarCross RefCross Ref
  62. Sanjiv Sharma, Zhenyi Huang, Michelle Rogers, Martyn Boutelle, and Anthony EG Cass. 2016. Evaluation of a minimally invasive glucose biosensor for continuous tissue monitoring. Analytical and bioanalytical chemistry 408, 29 (2016), 8427--8435.Google ScholarGoogle Scholar
  63. Joo Yong Sim, Chang-Geun Ahn, Eun-Ju Jeong, and Bong Kyu Kim. 2018. In vivo microscopic photoacoustic spectroscopy for non-invasive glucose monitoring invulnerable to skin secretion products. Scientific reports 8, 1 (2018), 1059.Google ScholarGoogle Scholar
  64. Jitendra Solanki, Om Prakash Choudhary, Pratima Sen, and Joseph Thomas Andrews. 2013. Polarization sensitive optical low-coherence reflectometry for blood glucose monitoring in human subjects. Review of Scientific Instruments 84, 7 (2013), 073114.Google ScholarGoogle ScholarCross RefCross Ref
  65. MJ Tierney, HL Kim, MD Burns, JA Tamada, and RO Potts. 2000. Electroanalysis of glucose in transcutaneously extracted samples. Electroanalysis: An International Journal Devoted to Fundamental and Practical Aspects of Electroanalysis 12, 9 (2000), 666--671.Google ScholarGoogle Scholar
  66. GT Tucker and MS Lennard. 1990. Enantiomer specific pharmacokinetics. Pharmacology & therapeutics 45, 3 (1990), 309--329.Google ScholarGoogle Scholar
  67. Sandeep Kumar Vashist. 2012. Non-invasive glucose monitoring technology in diabetes management: A review. Analytica chimica acta 750 (2012), 16--27.Google ScholarGoogle Scholar
  68. Sandeep Kumar Vashist, Dan Zheng, Khalid Al-Rubeaan, John HT Luong, and Fwu-Shan Sheu. 2011. Advances in carbon nanotube based electrochemical sensors for bioanalytical applications. Biotechnology advances 29, 2 (2011), 169--188.Google ScholarGoogle Scholar
  69. Jeanette M Waller and Howard I Maibach. 2005. Age and skin structure and function, a quantitative approach (I): blood flow, pH, thickness, and ultrasound echogenicity. Skin research and technology 11, 4 (2005), 221--235.Google ScholarGoogle Scholar
  70. Stuart Alan Weinzimer. 2004. Analysis: PENDRA: The Once and Future Noninvasive Continuous Glucose Monitoring Device? Diabetes technology & therapeutics 6, 4 (2004), 442--444.Google ScholarGoogle Scholar
  71. Michael FG Wood, Nirmalya Ghosh, Xinxin Guo, and I Alex Vitkin. 2008. Towards noninvasive glucose sensing using polarization analysis of multiply scattered light. Handbook of optical sensing of glucose in biological fluids and tissues 12 (2008).Google ScholarGoogle Scholar
  72. Jyoti Yadav, Asha Rani, Vijander Singh, and Bhaskar Mohan Murari. 2014. Near-infrared LED based non-invasive blood glucose sensor. In 2014 International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 591--594.Google ScholarGoogle ScholarCross RefCross Ref
  73. Kamal Youcef-Toumi and Vidi A Saptari. 1999. Noninvasive blood glucose analysis using near infrared absorption spectroscopy. The Home Automation and Healthcare Consortium (1999), 2--3.Google ScholarGoogle Scholar
  74. Chiara Zecchin, Andrea Facchinetti, Giovanni Sparacino, Chiara Dalla Man, Chinmay Manohar, James A Levine, Ananda Basu, Yogish C Kudva, and Claudio Cobelli. 2013. Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring. Diabetes technology & therapeutics 15, 10 (2013), 836--844.Google ScholarGoogle Scholar
  75. Huiting Zheng, Jiabin Yuan, and Long Chen. 2017. Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation. Energies 10, 8 (2017), 1168.Google ScholarGoogle Scholar

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        cover image ACM Conferences
        SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
        November 2020
        852 pages
        ISBN:9781450375900
        DOI:10.1145/3384419

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        • Published: 16 November 2020

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