LLM-Assisted Explainable Daily Stress Recognition: Physiologically Grounded Threshold Rules from PPG Features

Stress has become one of the most pervasive health challenges in modern societies, contributing to cardiovascular, cognitive, and emotional disorders that degrade overall well-being and productivity. Continuous monitoring of stress in everyday settings is thus critical for preventive healthcare. Recent advances in wearable sensing technologies, particularly photoplethysmography (PPG)-based devices, have enabled unobtrusive measurement of physiological signals linked to stress. However, the analysis of such data increasingly relies on deep learning models whose complex and non-transparent decision mechanisms limit clinical interpretability and user trust. To address this gap, this study introduces a novel LLM-assisted explainable framework that combines data-driven analysis of photoplethysmography (PPG) features with physiological reasoning. First, handcrafted cardiac variability features such as Root Mean Square of Successive Differences (RMSSD), high-frequency (HF) power, and the percentage of successive NN intervals differing by more than 50 ms (pNN50) are extracted from wearable PPG signals collected in daily conditions. After algorithmic threshold selection via ROC–Youden analysis, an LLM is used solely for physiological interpretation and literature-based justification of the resulting rules. The resulting transparent rule set achieves approximately 75% binary accuracy, rivaling CNN, LSTM, Transformer, and traditional ML baselines, while maintaining full interpretability and physiological validity. This work demonstrates that LLMs can function as scientific reasoning companions, bridging raw biosignal analytics with explainable, evidence-based models—marking a new step toward trustworthy affective computing.