- Research Article
- 10.1109/miot.2025.3639278
Simulation-Driven Design of a Non-Invasive Photoacoustic Glucose Monitoring Device With IoT Integration and Hybrid Energy Harvesting
- May 01, 2026
- IEEE Internet of Things Magazine
- Shivani Kumari + 5 more +5
The discomfort, invasiveness, and inconvenience associated with traditional glucose monitoring methods continue to hinder patient compliance and the effectiveness of diabetes management. This simulation-driven feasibility study presents the design of the Photoacoustic Glucose Monitoring Device (PAGMD), a non-invasive glucose sensing system that integrates photoacoustic spectroscopy, machine learning, and IoT connectivity to enable real-time glucose estimation. PAGMD employs pulsed near-infrared light to induce glucose-dependent acoustic signals, which are detected by ultrasonic transducers and analyzed by machine learning algorithms for concentration prediction. The device features a hybrid energy harvesting system, combining thermoelectric and piezoelectric elements, to support continuous, battery-assisted operation and long-term usability. The system was theoretically validated using a multi-stage simulation pipeline that included Monte Carlo photon modeling, finite element acoustic simulation, and synthetic glucose dataset generation across a range of physiological conditions. The machine learning models achieved a coefficient of determination of 0.98 and a mean absolute relative difference of 6.97%, with 98.2% of predictions falling within clinically acceptable error zones. While current findings are based on simulated environments, a future validation roadmap encompassing in vitro, ex vivo, and in vivo studies is proposed to support clinical translation. This work should be regarded as a simulation-driven feasibility study; the reported benchmarks represent design targets for future prototyping rather than experimentally validated hardware results. By prioritizing non-invasiveness, ease of use, and adaptive intelligence, PAGMD represents a promising step toward inclusive, personalized metabolic health monitoring, especially for individuals with disabilities or limited dexterity.
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