MUMBAI, India, March 13 -- Intellectual Property India has published a patent application (202641025425 A) filed by Madhankumar C; Vetrivale K M; Roshan Begum F; Muthumeena S; Niranjan S; and Gethzi J, Pollachi, Tamil Nadu, on March 4, for 'non-invasive glucometer using esp32 and iot.'
Inventor(s) include Vetrivale K M; Roshan Begum F; Muthumeena S; Niranjan S; and Gethzi J.
The application for the patent was published on March 13, under issue no. 11/2026.
According to the abstract released by the Intellectual Property India: "The Non-Invasive Glucometer project presents the design and development of a glucose estimation system that eliminates the need for skin penetration or blood extraction. Conventional glucose monitoring methods rely on invasive finger prick sampling, which causes discomfort, restricts frequent testing, and reduces patient compliance among diabetic individuals. This project proposes a non invasive optical sensing approach that leverages variations in infrared light absorption characteristics of skin tissue associated with glucose concentration levels. The proposed system integrates an ESP32 microcontroller with a MAX30102 optical sensor to measure photoplethysmographic (PPG) signals by analyzing infrared and red light absorption through human tissue. Variations in optical absorption patterns are processed using signal conditioning and algorithmic calibration techniques to estimate glucose trends. A capacitive touch sensor is incorporated to ensure accurate finger placement and initiate measurement only under stable contact conditions, thereby improving reliability and reducing motion artifacts. The ESP32 facilitates real-time data acquisition, processing, and wireless transmission via Wi-Fi to an IoT-based monitoring platform. The system classifies glucose levels into categories such as low, normal, and high based on calibrated threshold modeling. Prototype-level testing demonstrates the feasibility of non-invasive glucose trend estimation; however, accuracy is influenced by physiological factors including skin tone, hydration level, ambient temperature, and peripheral blood flow. This work establishes a foundational framework for non-invasive glucose monitoring and highlights the potential for future enhancements through machine learning-based calibration models, large-scale clinical validation, sensor fusion techniques, and integration into wearable healthcare devices for continuous monitoring applications."
Disclaimer: Curated by HT Syndication.
