Advertisement

Blood Sugar Level Indication Through Chewing and Swallowing from Acoustic MEMS Sensor and Deep Learning Algorithm for Diabetic Management

  • S. Krishna Kumari
  • J. M. Mathana
Patient Facing Systems
  • 14 Downloads
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health

Abstract

Diabetes, a metabolic disorder due to high blood glycemic index in the human body. The glycemic index varies in the human of improper diet and eating pattern such as junk foods, variation in the quantity of food, swallowing of food without chewing and stress. However, the diagnose of increase or decrease in the glycemic index is a challenging task. Similarly, the regulation of glycemic index without regular exercise is a major problem in day to day life. In this paper, we propose a novel SCS method to regulate glycemic index without exercise through changing the eating method. The proposed SCS eating method consists of Size of the food, Chewing style and Swallow time (SCS) of the food to regulate glycemic index. Furthermore, the proposed SCS method evaluate and validate through the acoustic signal acquired and processed with deep learning algorithm to analyze the chewing pattern of food to formulate a standard procedure for eating style and to reduce the glycemic level. The validation of diabetes done by measurement of blood glycemic through AccuChek Instant S Glucometer. Furthermore, the SCS method of eating style from 50 diabetes persons reduces the blood glucose level drastically by 85% after following the proposed method of eating style.

Keywords

Chewing Swallowing Acoustic sensor Diabetic measurement 

Notes

Compliance with Ethical Standards

Conflict of Interest

The authors have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Sazonov, E. S., and Fontana, J. M., A sensor system for automatic detection of food intake through non-invasive monitoring of chewing. IEEE Sensors Journal 12(5):1340–1348, 2012.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Xu, W. L., Pap, J.-S., and Bronlund, J., Design of a biologically inspired parallel robot for foods chewing. IEEE Transactions on Industrial Electronics 55(2):832–841, 2008.CrossRefGoogle Scholar
  3. 3.
    Emorine, M., Mielle, P., Maratray, J., Septier, C., Thomas-Danguin, T., and Salles, C., Use of sensors to measure in-mouth salt release during food chewing. IEEE Sensors Journal 12(11):3124–3130, 2012.CrossRefGoogle Scholar
  4. 4.
    Papapanagiotou, V., Diou, C., Zhou, L., van den Boer, J., Mars, M., and Delopoulos, A., A Novel Chewing Detection System Based on PPG, Audio, and Accelerometry. IEEE Journalof Biomedical and Health Informatics 21(3):607–618, 2017.CrossRefGoogle Scholar
  5. 5.
    Mata, A. D., Marques, D., Rocha, S., Francisco, H., Santos, C., Mesquita, M. F., and Singh, J., Effects of diabetes mellitus on salivary secretion and its composition in the human. Journal Of Molecular And Cellular Biochemistry 261(1-2):137–142, 2004.CrossRefPubMedGoogle Scholar
  6. 6.
    Buisson, J.-C., and Garel, A., Balancing meals using fuzzy arithmetic and heuristic search algorithms. IEEE Transactions on Fuzzy Systems 11(1):68–78, 2003.CrossRefGoogle Scholar
  7. 7.
    Komatsu, K., Hasegawa, H., Honda, T., Yabashi, A., and Kawasaki, T., Nerve Growth Factor in Saliva Stimulated by Mastication. Oral Science International 5(2):78–84, 2008.CrossRefGoogle Scholar
  8. 8.
    Anthimopoulos, M. M., Gianola, L., Scarnato, L., Diem, P., and Mougiakakou, S. G., A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE Journal of Biomedical Health Informatics 18(4):1261–1271, 2014.CrossRefPubMedGoogle Scholar
  9. 9.
    Päßler, S., and Wolf-Joachim, F., Food intake monitoring: Automated chew event detection in chewing sounds. IEEE Journal of Biomedical and Health Informatics 18(1):278–289, 2014.CrossRefPubMedGoogle Scholar
  10. 10.
    Lucisano, J. Y., Routh, T. L., Lin, J. T., and Gough, D. A., Glucose Monitoring in Individuals with Diabetes Using a Long-Term Implanted Sensor/Telemetry System and Model. IEEE Transactions on Biomedical Engineering 64(9):1982–1993, 2017.CrossRefPubMedGoogle Scholar
  11. 11.
    Temiloluwa (Olubanjo) Prioleau, Elliot Moore II, and Maysam Ghovanloo., Unobtrusive and Wearable Systems for Automatic Dietary Monitoring. IEEE Transactions on Biomedical Engineering. 64( 9): 2075–2089, 2017.Google Scholar
  12. 12.
    Farooq, M., and Sazonov, E., Segmentation and Characterization of Chewing Bouts by Monitoring Temporalis Muscle Using Smart Glasses with Piezoelectric Sensor. IEEE Journal of Biomedical and Health Informatics 21(6):1495–1503, 2017.CrossRefPubMedGoogle Scholar
  13. 13.
    Huang, Q., Wang, W., and Zhang, Q., Your Glasses Know Your Diet: Dietary Monitoring Using Electromyography Sensors. IEEE Internet of Things Journal 4(3):705–712, 2017.CrossRefGoogle Scholar
  14. 14.
    Farooq, M., and Sazonov, E., Accelerometer-Based Detection of Food Intake in Free-Living Individuals. IEEE Sensors Journal 18(9):3752–3758, 2018.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of ECEVel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering CollegeChennaiIndia
  2. 2.Department of ECEMangalam College Of EngineeringKottayamIndia

Personalised recommendations