Smartphone Enabled Wearable Diabetes Monitoring System
Glucose is a vital biomarker involved in numerous physiological and pathological processes, making its detection a key factor in diabetes monitoring. To enhance convenience, real-time tracking, comfort, and accuracy, glucose measurement techniques are rapidly advancing towards smartphone integrated wearable, non-invasive, and low power systems. In this work, a diabetes monitoring system is developed which can be used as a wearable health tracker. The device offers dual ways of monitoring glucose levels in diabetic patients; first by glucose measurement via blood using a screen-printed electrode sensor (SPES), second by volatile organic compounds (VOCs) measurement via breath analysis using an ethanol sensor. Additionally, an oximetry sensor is included in the system to monitor important human vitals such as heart rate and blood oxygen saturation (SpO2), similar to commercial health trackers. The system is fabricated on a printed circuit board and its size is 3 cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 3$ </tex-math></inline-formula> cm which is suitable as a wearable device. It also consumes low power and has capabilities of wired/wireless charging and wired/wireless communication. Furthermore, customized Windows and Android applications have been developed to visualize the obtained data in user’s computer or smartphone. The conducted experiments aimed at validating the performance of each sensors have yielded promising results. These findings indicate that the developed system has significant potential to be utilized as a point-of-care testing device for real-time diabetes monitoring.
Authors
- Shanthala Lakshminarayana (ORCID: https://orcid.org/0000-0002-1058-1924)
- Hyusim Park (ORCID: https://orcid.org/0000-0001-8190-0016)
- Yuze Sun (ORCID: https://orcid.org/0000-0001-5149-2277)
- Mrudula Ranganatha
Institutions
- National Society of Professional Engineers (US)
- The University of Texas at Arlington (US)
- University of North Texas at Dallas (US)
Publication Details
- Journal
- IEEE Access
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/access.2025.3552731
- Citations
- 3
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 8.81