Clinical Validation of a Non-Invasive System for Monitoring Physiological Parameters in Humans Based on the Internet of Things and Machine Learning

Resumen

The monitoring of physiological parameters in individuals undergoing frequent clinical follow-up continues to rely, in many cases, on invasive procedures or equipment that makes continuous monitoring outside the hospital setting difficult. In light of this situation, the present study evaluated a non-invasive system for measuring glucose, oxygen saturation, heart rate, and body temperature, supported by the Internet of Things and machine learning. The research was conducted in 2025 at a private clinic in the Buena Fe canton, Los Ríos province, Ecuador, using a quantitative, exploratory, descriptive, and quasi-experimental design, with a sample of 120 patients selected from a population of 174 users. Data collection was performed under controlled conditions, and each measurement obtained by the system was compared with values from commercial reference devices. For predictive processing, models such as linear regression, support vector machines, decision trees, and neural networks were analyzed and evaluated using performance metrics including R², MSE, RMSE, accuracy, F1-score, and AUC. The findings showed that the system performed well in estimating physiological signals, with better performance in the more complex machine learning-based models. The neural network achieved the highest performance indicators, with an accuracy of 95.42% and an F1-score of 98.13%, while the system exhibited low percentage errors in the clinical comparison for oxygen saturation.

It is concluded that non-invasive physiological monitoring supported by IoT and machine learning constitutes a viable option for continuous clinical monitoring, with potential for use in telemedicine and in healthcare settings with limited resources.