Personalized Health Interventions Using AI and Wearable Data: A Data Science Pipeline Approach

Abstract

The convergence of artificial intelligence (AI), wearable technology, and data science is reshaping the future of personalized healthcare. Wearable devices now generate massive streams of continuous, multimodal physiological data that, when harnessed appropriately, can enable real-time, predictive, and individualized health interventions. This article develops a comprehensive data science pipeline for personalized health interventions, integrating data acquisition, preprocessing, feature engineering, deep learning, causal inference, privacy-preserving analytics, and clinical deployment. Drawing on cross-disciplinary research in AI, health informatics, and biomedical engineering, the study highlights both methodological advancements and translational challenges. The pipeline is designed to optimize scalability, accuracy, interpretability, and regulatory compliance, providing a blueprint for next-generation digital health ecosystems.