Abstract:

Wearable technology is progressively becoming an integral component of health monitoring, harvesting real-time physiological information and allowing constant health analysis. Drawing on this information using Natural Language Processing (NLP) provides new opportunities to provide personalized health advice. Existing systems are, however, commonly incompetent in context-driven understanding of health data and generally provide generic advice that is not personalized or dynamically responsive to users' fluctuating bodily or emotional conditions. These constraints lower the performance and user acceptance of wearable-based healthcare systems. To overcome these issues, we introduce a new framework known as Personalized Stress Management using NLP-based Transformer for Health Context Extraction (PSM-NLP-Trex). The model uses transformer-based NLP methods to semantically examine both structured wearable data and unstructured inputs in order to obtain rich health contexts. The system uses context-aware attention mechanisms to detect stress-related patterns and adapt interventions dynamically. Experimental assessments indicate PSM-NLP-Trex outperforms conventional rule-based systems by a large margin in the areas of user satisfaction, stress level minimization, and contextual coherence of suggestions. The results validate the promise of transformer-based NLP models in improving the accuracy and customization of wearable-based healthcare assistance systems.