An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi
Source abstract
Abstract
Patients diagnosed with diabetes mellitus must monitor their blood glucose levels in order to control the glycaemia. Consequently, they must perform a capillary test at least three times per day and, besides that, a laboratory test once or twice per month. These standard methods pose difficulty for patients since they need to prick their finger in order to determine the glucose concentration, yielding discomfort and distress. In this paper, an Internet of Things (IoT)-based framework for non-invasive blood glucose monitoring is described. The system is based on Raspberry Pi Zero (RPi) energised with a power bank, using a visible laser beam and a Raspberry Pi Camera, all implemented in a glove. Data for the non-invasive monitoring is acquired by the RPi Zero taking a set of pictures of the user fingertip and computing their histograms. Generated data is processed by an artificial neural network (ANN) implemented on a Flask microservice using the Tensorflow libraries. In this paper, all measurements were performed in vivo and the obtained data was validated against laboratory blood tests by means of the mean absolute error (10.37%) and Clarke grid error (90.32% in zone A). Estimated glucose values can be harvested by an end device such as a smartphone for monitoring purposes.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Spectroscopy Techniques in Biomedical and Chemical Research · IoT and Edge/Fog Computing · Non-Invasive Vital Sign Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Facultad de Ingeniería, Universidad Autónoma de Guerrero, Chilpancingo 39087, Mexico
Facultad de Ciencias Químico–Biológicas, Universidad Autónoma de Guerrero, Chilpancingo 39087, Mexico
Division of Medical Engineering Research, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Mexico City 14389, Mexico
References (50)
Show source-provided references
1. An approach to develop the smart health using Internet of Things and authentication based on biometric technology10.1016/j.future.2018.09.024 ↗
2. Internet of Things (IoT): A vision, architectural elements, and future directions10.1016/j.future.2013.01.010 ↗
3. The Internet of Things vision: Key features, applications and open issues10.1016/j.comcom.2014.09.008 ↗
4. Huang, L.-S., Su, J.-Y., and Pao, T.-L. (2019). A context aware smart classroom architecture for smart campuses. Appl. Sci., 9.10.3390/app9091837 ↗
5. Kim, Y., Moon, J., and Hwang, E. (2018). Constructing differentiated educational materials using semantic annotation for sustainable education in IoT environments. Sustainability, 10.10.3390/su10041296 ↗
6. Intelligent edge computing for IoT-Based energy management in smart cities10.1109/MNET.2019.1800254 ↗
7. Energy-efficient resource allocation for industrial cyber-physical IoT systems in 5G era10.1109/TII.2018.2799177 ↗
8. COLLECT: COLLaborativE ConText-aware service oriented architecture for intelligent decision-making in the Internet of Things10.1016/j.eswa.2017.05.034 ↗
9. The Internet of Things (IoT): Applications, investments, and challenges for enterprises10.1016/j.bushor.2015.03.008 ↗
10. PortoLivingLab: An IoT-Based Sensing Platform for Smart Cities10.1109/JIOT.2018.2791522 ↗
11. Efficient IoT-based sensor BIG Data collection—Processing and analysis in smart buildings10.1016/j.future.2017.09.082 ↗
12. Exploiting IoT and big data analytics: Defining Smart Digital City using real-time urban data10.1016/j.scs.2017.12.022 ↗
13. Lai, X., Yang, T., Wang, Z., and Chen, P. (2019). IoT Implementation of kalman filter to improve accuracy of air quality monitoring and prediction. Appl. Sci., 9.10.3390/app9091831 ↗
14. Benammar, M., Abdaoui, A., Ahmad, S., Touati, F., and Kadri, A. (2018). A modular IoT platform for real-time indoor air quality monitoring. Sensors, 18.10.3390/s18020581 ↗
15. Internet of things mobile-air pollution monitoring system (IoT-Mobair)10.1109/JIOT.2019.2903821 ↗
16. Development of a smart mobile data module for fetal monitoring in E-Healthcare10.1007/s10916-018-0938-1 ↗
17. Edge-based compression and classification for smart healthcare systems: Concept, implementation and evaluation10.1016/j.eswa.2018.09.019 ↗
18. Improving the quality of health care in the United Kingdom and the United States: A framework for change10.1111/1468-0009.00206 ↗
19. MIAPS: A web-based system for remotely accessing and presenting medical images10.1016/j.cmpb.2013.09.008 ↗
20. Segura-Garcia, J., Garcia-Pineda, M., Tamarit-Tronch, M., Cibrian, R., and Salvador-Palmer, R. (2018). Cost-Effective eHealth system based on a Multi-Sensor System-on-Chip platform and data fusion in cloud for sport activity monitoring. Electronics, 7.10.3390/electronics7090183 ↗
21. Rohokale, V.M., Prasad, N.R., and Prasad, R. (March, January 28). A cooperative Internet of Things (IoT) for rural healthcare monitoring and control. Proceedings of the 2011 2nd International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE), Chennai, India.10.1109/WIRELESSVITAE.2011.5940920 ↗
22. Doukas, C., and Maglogiannis, I. (2012, January 4–6). Bringing IoT and cloud computing towards pervasive healthcare. Proceedings of the 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Palermo, Italy.10.1109/IMIS.2012.26 ↗
23. (2019, July 27). WHO: World Health Organization Diabetes Factsheets. Available online: https://www.who.int/en/news-room/fact-sheets/detail/diabetes.
24. The International Diabetes Federation diabetes atlas methodology for estimating global and national prevalence of diabetes in adults10.1016/j.diabres.2011.10.040 ↗
25. Non-invasive glucose monitoring: A review of challenges and recent advances10.19080/CTBEB.2017.06.555696 ↗
26. Lanthanide ions doped in vanadium oxide for sensitive optical glucose detection10.1364/OME.8.003277 ↗
27. Powering electronic contact lenses: Current achievements, challenges, and perspectives10.1586/17469899.2014.922873 ↗
28. Current development in non-invasive glucose monitoring10.1016/j.medengphy.2007.06.003 ↗
29. Non-invasive glucose monitoring technology in diabetes management: A review10.1016/j.aca.2012.03.043 ↗
30. Recent advances in noninvasive glucose monitoring
31. Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy10.1016/j.bspc.2015.01.005 ↗
32. Mid-Infrared laser applications in medicine and biology10.1098/rsta.2000.0747 ↗
33. Non-invasive glucose monitoring: Assessment of technologies and devices according to quantitative criteria10.1016/j.diabres.2006.10.027 ↗
34. Comparison between transmittance and reflectance measurements in glucose determination using near infrared spectroscopy10.1117/1.2165572 ↗
35. Noninvasive measurement of glucose concentration on human fingertip by optical coherence tomography10.1117/1.JBO.23.4.047001 ↗
36. Shao, J., Lin, M., Li, Y., Li, X., Liu, J., Liang, J., and Yao, H. (2012). In vivo blood glucose quantification using Raman spectroscopy. PLoS ONE, 7.10.1371/journal.pone.0048127 ↗
37. Novel approach to non-invasive blood glucose monitoring based on transmittance and refraction of visible laser light10.1109/ACCESS.2017.2707384 ↗
38. The modified Beer—Lambert law revisited10.1088/0031-9155/51/5/N02 ↗
39. Francisco-García, V., Guzmán-Guzmán, I.P., Salgado-Rivera, R., Alonso-Silverio, G.A., and Alarcón-Paredes, A. (2019, January 18). Non-invasive Glucose Level Estimation: A Comparison of Regression Models Using the MFCC as Feature Extractor. Proceedings of the Mexican Conference on Pattern Recognition, Querétaro, México.10.1007/978-3-030-21077-9_19 ↗
40. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv.
41. (2019, July 16). Pallets Projects Flask. Available online: https://flask.palletsprojects.com.
42. A non-invasive glucose level estimation in a multi-sensing health care monitoring system10.3233/THC-171075 ↗
43. Mann, H.B., and Whitney, D.R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat., 50–60.10.1214/aoms/1177730491 ↗
44. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press.
45. Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
46. The original Clarke error grid analysis (EGA)10.1089/dia.2005.7.776 ↗
47. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.10.1007/978-1-4614-7138-7 ↗
48. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques10.1016/j.artmed.2011.05.001 ↗
49. Device and method for noninvasive glucose assessment10.1177/1932296818763457 ↗
50. Noninvasive glucose monitoring: Increasing accuracy by combination of multi-technology and multi-sensors10.1177/193229681000400312 ↗