Next-gen non-invasive glucose monitoring using microwave sensors and ai-based thumb positioning analysis
| dc.contributor.author | Uma, S. | |
| dc.contributor.author | Soundharya, P. | |
| dc.contributor.author | Charisma, S. | |
| dc.contributor.author | Jackson, Beulah | |
| dc.contributor.author | Ganapathi, G. | |
| dc.contributor.author | Ali, Guma | |
| dc.date.accessioned | 2025-12-18T16:42:06Z | |
| dc.date.available | 2025-12-18T16:42:06Z | |
| dc.date.issued | 2025-06-25 | |
| dc.description | This study introduces a next-generation non‑invasive glucose monitoring system leveraging microwave sensors and AI to overcome discomfort and accuracy limitations of finger‑prick and optical methods. By integrating advanced signal processing and machine learning, the system offers continuous, reliable glucose readings, enhancing personal health management and reducing complications. It directly supports SDG 3 (good health and well‑being) and contributes to SDG 9 (innovation in health technologies). Although not water‑ or infrastructure‑focused, the paper aligns with Uganda’s NDP IV vision for improving healthcare delivery through technological innovation, supporting healthier populations and strengthening national health systems. | |
| dc.description.abstract | Because of discomfort and accuracy problems, the existing non-invasive glucose monitoring devices include optical sensors and finger-prick tests have long been under doubt for their viability. Variations in skin type and environmental interference might make these techniques unreliable. To get past these constraints, the study presents a fresh approach combining artificial intelligence (AI)-based thumb positioning analysis with microwave sensors. By spotting changes in the dielectric characteristics of the skin brought on by glucose, the microwave sensors offer exact, invasive, real-time readings. Through best sensor alignment, AI removes thumb position errors. Performance assessment indicates substantially better outcomes than the existing systems with an R2 of 0.98, RMSE of 7.1 mg/dL, and MAE of 5.2 mg/dL. The great flexibility of the proposed system to a wide spectrum of demographics and excellent user compliance (95%) underlines its possibility for efficient and comfortable diabetes control. The technique represents a substantial development in non-invasive glucose monitoring. | |
| dc.identifier.citation | Uma, S., Soundharya, P., Charisma, S., Jackson, B., Ganapathi, G., & Ali, G. (2025, May). Next-gen non-invasive glucose monitoring using microwave sensors and ai-based thumb positioning analysis. In 2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE): 1-7. | |
| dc.identifier.uri | https://dir.muni.ac.ug/handle/20.500.12260/842 | |
| dc.language.iso | en | |
| dc.publisher | IEEE | |
| dc.subject | Temperature measurement | |
| dc.subject | Accuracy | |
| dc.subject | Thumb | |
| dc.subject | Microwave sensors | |
| dc.subject | Skin | |
| dc.subject | Real-time systems | |
| dc.subject | Glucose | |
| dc.subject | Diabetes | |
| dc.subject | Artificial intelligence | |
| dc.subject | Monitoring | |
| dc.title | Next-gen non-invasive glucose monitoring using microwave sensors and ai-based thumb positioning analysis | |
| dc.type | Other |