Non-Invasive Blood Glucose Meter

Authors

  • Mahesh Sheth
  • Makarand Jadhav
  • Sunita Deshmukh

DOI:

https://doi.org/10.46243/jst.2021.v6.i04.pp67-74

Keywords:

ML, ML.Ne, WiFi

Abstract

The glucose concentration in the range from 111-330 mg/dl represents a diabetic patient. it causes physical impairment of vital organs. presently, non-invasive blood glucose monitoring device that uses the blood dielectric and impedance properties is becoming popular for diabetes detection. a realistic reusable low-cost non-invasive glucose meter is developed using wi-fi module. it measures glucose level based on the permittivity changes in human blood with sub-carriers. each sub-carrier is spaced with 312.5 khz bandwidth, generating 64 amplitudes and phases is used for channel estimation. a hampel filter is used to suppress abrupt amplitude variations occurring due to the environment effects. the system is trained using ml.net fast forest quantile regression method. the accuracy obtained is around 90%.

 

References

Centers for Disease Control and Prevention (CDC. (2004). Prevalence of overweight and obesity among adults with diagnosed diabetes--United States, 1988-1994 and 1999-2002. MMWR. Morbidity and mortality weekly report, 53(45), 1066-1068

Church, V. (2003). Economic costs of diabetes in the US in 2002. Diabetes care, 26(3), 917-932

American Diabetes Association. (2010). Standards of medical care in diabetes—2010. Diabetes care, 33(Supplement 1), S11-S61

Nathan, D. M., Kuenen, J., Borg, R., Zheng, H., Schoenfeld, D., & Heine, R. J. (2008). Translating the A1C assay into estimated average glucose values. Diabetes care, 31(8), 1473-1478

Kaufman, F. R., Gibson, L. C., Halvorson, M., Carpenter, S., Fisher, L. K., & Pitukcheewanont, P. (2001). A pilot study of the continuous glucose monitoring system: clinical decisions and glycemic control after its use in pediatric type 1 diabetic subjects. Diabetes care, 24(12), 2030-2034

Newman, J. D., & Turner, A. P. (2005). Home blood glucose biosensors: a commercial perspective. Biosensors and bioelectronics, 20(12), 2435-2453

Tamada, J. A., Garg, S., Jovanovic, L., Pitzer, K. R., Fermi, S., Potts, R. O.,... & Cygnus Research Team. (1999). Noninvasive glucose monitoring: comprehensive clinical results. Jama, 282(19), 1839-1844

Larin, K. V., Eledrisi, M. S., Motamedi, M., & Esenaliev, R. O. (2002). Noninvasive blood glucose monitoring with optical coherence tomography: a pilot study in human subjects. Diabetes care, 25(12), 2263-2267

Islam, M., Ali, M. S., Shoumy, N. J., Khatun, S., Karim, M. S. A., & Bari, B. S. (2020). Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence. SN Applied Sciences, 2(2), 1-9

Baghelani, M., Abbasi, Z., Daneshmand, M., & Light, P. E. (2020). Non-invasive continuous-time glucose monitoring system using a chipless printable sensor based on split ring microwave resonators. Scientific Reports, 10(1), 1-15

Jain, P., Joshi, A. M., Agrawal, N., & Mohanty, S. (2020). iGLU 2.0: A new non-invasive, accurate serum glucometer for smart healthcare. arXiv preprint arXiv:2001.09182

Islam, M., Min, C. L., Shoumy, N. J., Ali, M. S., Khatun, S., Karim, M. S. A., & Bari, B. S. (2020, May). Software module development for noninvasive blood glucose measurement using an ultra-wide band and machine learning. In Journal of Physics: Conference Series (Vol. 1529, No. 5, p. 052066). IOP Publishing

Pimentel, S., Agüero, P. D., Uriz, A. J., Bonadero, J. C., Liberatori, M., & Moreira, J. C. (2013, December). Simulation of a non-invasive glucometer based on a microwave resonator sensor. In Journal of Physics: Conference Series (Vol. 477, No. 1, p. 012020). IOP Publishing

Geng, Z., Tang, F., Ding, Y., Li, S., & Wang, X. (2017). Noninvasive continuous glucose monitoring using a multisensor-based glucometer and time series analysis. Scientific reports, 7(1), 1-10

Yeh, S. J., Hanna, C. F., & Khalil, O. S. (2003). Monitoring blood glucose changes in cutaneous tissue by temperature-modulated localized reflectance measurements. Clinical chemistry, 49(6), 924-934

Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). New York: Springer series in statistics

Ahmed, Z., Amizadeh, S., Bilenko, M., Carr, R., Chin, W. S., Dekel, Y.,... & Zhu, Y. (2019, July). Machine learning at microsoft with ml. net. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2448-2458)

Reis, I., Baron, D., & Shahaf, S. (2018). Probabilistic random forest: A machine learning algorithm for noisy data sets. The Astronomical Journal, 157(1), 16

Bogner, K., Pappenberger, F., & Zappa, M. (2019). Machine learning techniques for predicting the energy consumption/production and its uncertainties driven by meteorological observations and forecasts. Sustainability, 11(12), 3328

Zahid, M., Chen, Y., Jamal, A., & Mamadou, C. Z. (2020). Freeway short-term travel speed prediction based on data collection time-horizons: A fast forest quantile regression approach. Sustainability, 12(2), 646

Yang, J., Liu, Y., Liu, Z., Wu, Y., Li, T., & Yang, Y. (2021). A Framework for Human Activity Recognition Based on WiFi CSI Signal Enhancement. International Journal of Antennas and Propagation, 2021

Hu, P., Yang, W., Wang, X., & Mao, S. (2019, December). MiFi: Device-Free Wheat Mildew Detection Using Off-the-Shelf WiFi Devices. In 2019 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE

Deng, J., Sun, W., Guan, L., Zhao, N., Khan, M. B., Ren, A.,... & Abbasi, Q. H. (2019). Noninvasive suspicious liquid detection using wireless signals. Sensors, 19(19), 4086

Dang, X., Cao, Y., Hao, Z., & Liu, Y. (2020). WiGId: Indoor Group Identification with CSI-Based Random Forest. Sensors, 20(16), 4607

Ma, Y., Zhou, G., & Wang, S. (2019). WiFi sensing with channel state information: A survey. ACM Computing Surveys (CSUR), 52(3), 1-36

Song, H., Wei, B., Yu, Q., Xiao, X., & Kikkawa, T. (2020). WiEps: Measurement of Dielectric Property With Commodity WiFi Device—An Application to Ethanol/Water Mixture. IEEE Internet of Things Journal, 7(12), 11667-11677

Tan, S., Zhang, L., & Yang, J. (2018, July). Sensing fruit ripeness using wireless signals. In 2018 27th International Conference on Computer Communication and Networks (ICCCN) (pp. 1-9). IEEE

Li, F., Valero, M., Shahriar, H., Khan, R. A., & Ahamed, S. I. (2021). Wi-COVID: A COVID-19 symptom detection and patient monitoring framework using WiFi. Smart Health, 19, 100147

Atif, M., Muralidharan, S., Ko, H., & Yoo, B. (2020). Wi-ESP—A tool for CSI-based Device-Free Wi-Fi Sensing (DFWS). Journal of Computational Design and Engineering, 7(5), 644-656

Hernandez, S. M., & Bulut, E. (2020, March). Performing WiFi sensing with off-the-shelf smartphones. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 1-3). IEEE

Makrand M Jadhav, Gajanan H. Chavan, and Altaf O. Mulani, “Machine Learning based Autonomous Fire Combat Turret”, Turkish Journal of Computer and Mathematics Education, Vol.12 No.2 (2021), 2372-2381, https://doi.org/10.17762/turcomat.v12i2.2025

Published

2021-08-16

How to Cite

Mahesh Sheth, Makarand Jadhav, & Sunita Deshmukh. (2021). Non-Invasive Blood Glucose Meter. Journal of Science & Technology , 6(Special Issue 1), 67–74. https://doi.org/10.46243/jst.2021.v6.i04.pp67-74