Wearable smart sensors integration with AI and machine learning for tracking human health

This study comprehensively investigates the integration of wearable smart sensors with artificial intelligence (AI) and machine learning (ML) for human health tracking, focusing on seven major sensor types: sweat, glucose, wound, mental state monitoring, inhalation, CRISPR‒Cas, and quantum sensors. It elaborates on their design principles, detection mechanisms, and biomedical applications, as well as their respective advantages and inherent challenges. This study highlights the pivotal role of AI/ML in optimizing sensor performance, including enhancing detection sensitivity, processing complex data, enabling real-time analysis, and enabling personalized healthcare. Specifically, AI/ML facilitates noise filtering, pattern recognition, multibiomarker identification, and predictive diagnostics across different sensor systems. Despite significant advancements, the field is still confronted with challenges, including sensor stability, data security risks, high production costs, and biocompatibility issues. The paper concludes by outlining future research and development directions, emphasizing material innovation, algorithm optimization, and multimodal sensing integration, and strengthening clinical translation to fully unlock the potential of wearable sensors in proactive and personalized healthcare, ultimately contributing to the improvement of public health outcomes.