Heni Sumarti, Alvania Nabila Tasyakuranti, Qolby Sabrina

Pilot Study: Portable Non-Invasive Blood Sugar, Cholesterol, Uric Acid Monitoring System

Introduction

Pilot study: portable non-invasive blood sugar, cholesterol, uric acid monitoring system. Portable, non-invasive system monitors blood sugar, cholesterol, uric acid. Track health remotely with TCRT5000 sensor & Telegram, no blood samples. AI to boost accuracy.

Abstract

Degenerative diseases commonly associated with abnormal blood sugar, cholesterol, and uric acid levels require regular monitoring. Remote health monitoring technology enables children to monitor their parents' health conditions from a distance. This research presents a prototype development through Research and Development (R&D) methodology. This study developed a portable, low-cost, non-invasive detection system for blood sugar, cholesterol, and uric acid levels using the TCRT5000 sensor with Telegram integration. The compact device offers real-time monitoring advantages without blood sampling. The development followed the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. The research results show the prototype's coefficient of determination for blood sugar is 0.9733, cholesterol is 0.9411, and uric acid is 0.9610. The non-invasive prototype demonstrates measurement errors of 7.41% for blood sugar, 15.83% for cholesterol, and 14.69% for uric acid. These error rates currently exceed medical measurement standards. The system successfully integrates with the Telegram application for remote monitoring. Future research should incorporate artificial intelligence algorithms to minimize error values.

Review

This pilot study introduces an ambitious and highly relevant prototype for a portable, non-invasive multi-parameter monitoring system targeting blood sugar, cholesterol, and uric acid. The initiative to develop a low-cost, non-invasive device for essential health markers, particularly with remote monitoring capabilities via Telegram, addresses a significant unmet need in managing degenerative diseases and facilitating elderly care. The application of a structured R&D methodology, specifically the ADDIE model, indicates a systematic approach to development. The reported high coefficients of determination (0.9733 for blood sugar, 0.9411 for cholesterol, and 0.9610 for uric acid) are encouraging, suggesting a strong correlation between the device's readings and actual values, which is a promising preliminary indicator for a novel sensing approach. However, the reported measurement error rates significantly temper the initial enthusiasm. With errors of 7.41% for blood sugar, 15.83% for cholesterol, and 14.69% for uric acid, the device demonstrably exceeds current medical measurement standards. This is a critical limitation, as accuracy is paramount for diagnostic and monitoring tools in healthcare. The abstract's brevity also leaves crucial questions unanswered regarding the physiological mechanism by which a TCRT5000 sensor, typically an optical reflective sensor, can reliably and non-invasively detect these diverse biochemical analytes. Further detail on the sensor's interaction with the biological medium and the signal processing involved would be essential to understand the scientific basis for these measurements and address potential concerns about specificity and interference. Despite the current limitations in accuracy, the study lays foundational groundwork for future research. The authors correctly identify the need for incorporating artificial intelligence algorithms to minimize error values, which is a sensible next step. Beyond algorithmic improvements, future work must focus on rigorous clinical validation against gold-standard laboratory methods with larger, diverse patient cohorts. It would also be beneficial to explore alternative or complementary sensor technologies if the TCRT5000 proves insufficient for achieving clinical accuracy. While the concept is commendable and the prototype demonstrates potential, substantial improvements in measurement reliability and a deeper scientific exposition of the detection principle are imperative before this technology can be considered for real-world medical application.

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