Multimodal Sensing and Artificial Intelligence–Driven Data Fusion in Wearable Health Technologies, Advances, System Challenges, and Research Frontiers

Abstract

Multimodal sensing and data fusion using artificial intelligence have transformed wearable health technologies by integrating various physiological signals for continuous health monitoring. This study provides a comprehensive perspective on wearable technologies, such as fusion hierarchies, machine learning interpretation, and deployment in wearable body area networks. The research offers a performance assessment and structural issues affecting reliability, including sensor diversity, dominance, noise, and intermittent data loss due to motion artifacts and dropouts. As a result, it explores multimodal fusion failure modes, demonstrating how asynchronous failure and partial observability can cause instability in multimodal representations. The study also highlights the transition from continuous to event- and window-based processing to improve energy, computational, and clinical efficiency in edge computing. The study explores emerging approaches like self-supervised learning, multimodal foundation models, and digital twin-based personalized health models for enhancing robustness and generalization. The research also considers advances in functional materials, such as mechanochromic materials and biointegrated sensing platforms, to enable intuitive and seamless physiological monitoring. Finally, the work considers the regulatory, energy, and system constraints on these innovations and notes that future wearable systems must combine computational intelligence with physical form factors, interpretability, and safety, with robustness and adaptability being the guiding design principles. Received: 20 March 2026 | Revised: 22 May 2026 | Accepted: 1 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Najeem Olawale Adelakun: Conceptualization, Methodology, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Matthew Babatunde Olajide: Methodology, Validation, Formal analysis, Data curation, Writing – review & editing, Supervision, Project administration. Samuel Adeniyi Omolola: Formal analysis, Investigation, Resources, Data curation, Project administration.