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Description

Type 2 Diabetes Mellitus is frequently accompanied by peripheral microvascular disease, leading to impaired lower limb perfusion, impaired healing, and vulnerability to ulcers and amputation. The existing medical devices are predominantly aimed at systemic glucose control and do not have local therapeutic intervention to improve circulation or allow muscular glucose delivery. To meet this crucial demand, we have developed a portable, two-in-one biofeedback device that combines non-invasive blood glucose monitoring and Electronic Muscle Stimulation (EMS) therapy for the calf muscle. The glucose monitoring subsystem implements near-infrared (NIR) spectroscopy to measure skin light absorptance on the index finger. Real-time blood glucose concentration is achieved by applying a linear regression machine learning model. In parallel, the EMS subsystem delivers programmable electric pulses with adjustable frequency and waveforms to induce calf muscle contraction, promoting better blood flow and permitting physiological glucose utilization. The device is powered by an Arduino Uno microcontroller with manual and automatic modes of operation for diverse therapeutic needs. Prototype validation was verified using a logic gate analyzer to ensure pulse width integrity; concurrently, glucose monitoring achieved a marginal error rate of 30%. To enhance therapeutic precision, an artificial intelligence model analyzes user-specific monitoring data in real time and adjusts stimulation parameters autonomously to deliver personalized treatment. This customizable, dual-function platform device offers a promising pathway for non-pharmacological management and intervention against diabetic microvascular complications.

Publisher Location

Las Vegas (Nev.)

Publication Date

Fall 11-21-2025

Publisher

University of Nevada, Las Vegas

Language

English

Keywords

Diabetes Mellitus; Electronic Muscle Stimulation; Glucose Monitoring; Near-infrared spectroscopy; Arduino Uno

Disciplines

Electrical and Computer Engineering | Engineering

File Format

PDF

File Size

1300 KB

Permissions

Google Drive\Institutional Repository\OUR_OfficeOfUGResearch\Symposia\2025 Fall Symposium

Comments

Mentor: Shengjie (Patrick) Zhai

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

Real-Time AI-Enabled Therapeutic Device for Improving Peripheral Perfusion and Glucose Regulation in Type 2 Diabetes


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