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A comprehensive review on AI-powered wearable biosensors, highlighting how machine learning and edge AI enable real-time health monitoring and personalized care, including digital twins, LLMs, and challenges in privacy, scalability, and clinical integration.

Proposed a novel framework for generating CFs using large language models (LLMs), with a focus on structured sensor-derived datasets in health and physiological monitoring

The CAN-STRESS dataset provides multimodal physiological and self-reported data from 82 participants (39 cannabis users and 43 non-users) collected in real-world conditions using Empatica E4 wristbands. Preliminary analysis shows machine learning models can distinguish users from non-users with high accuracy, with electrodermal activity and heart rate emerging as key predictors.