A Quantum-Inspired Bio-Semiconductive Fascia Model for Stress and Hydration Monitoring Using XGBoost in Wearable Devices

This study introduces a quantum-inspired biosemiconductive fascia model for real-time monitoring of stress and hydration in wearable technology. The model conceptualizes fascia as a semiconductor influenced by quantum mechanics, using the electrical and hydration-dependent attributes of collagen and exclusion zone (EZ) water. Physiologically relevant features are extracted from this model for input into an XGBoost classifier, enabling precise detection of stress and hydration. Evaluation in a simulated biological dataset yielded an overall precision of 92. 5 %, with precision at 91.8 %, recall at 93.1 %, and an F1 score of 92. 4 %. For comparison, traditional algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) were employed, and XGBoost exceeded them by a minimum of 5 % in both accuracy and noise resilience. The incorporation of quantum-inspired signal propagation enhances sensitivity over standard physiological detection methods such as heart rate variability (HRV) and galvanic skin response (GSR). This research illustrates the promise of merging quantum-inspired biophysical modeling with advanced machine learning to develop wearable systems for reliable, continuous monitoring of stress and hydration, thus advancing personalized healthcare technologies.