Artificial Intelligence-driven Intelligent Wearable Systems: A full-stack Integration from Material Design to Personalized Interaction
Intelligent wearable systems are at the forefront of precision medicine and play a crucial role in enhancing human-machine interaction. Traditional devices ofte...
Abstract
Intelligent wearable systems are at the forefront of precision medicine and play a crucial role in enhancing human-machine interaction. Traditional devices often encounter limitations due to their dependence on empirical material design and basic signal processing techniques. To overcome these issues, we introduce the concept of Human-Symbiotic Health Intelligence (HSHI), which is a framework that integrates multi-modal sensor networks with edge-cloud collaborative computing and a hybrid approach to data and knowledge modeling. HSHI is designed to adapt dynamically to both inter-individual and intra-individual variability, transitioning health management from passive monitoring to an active collaborative evolution. The framework incorporates AI-driven optimization of materials and micro-structures, provides robust interpretation of multi-modal signals, and utilizes a dual mechanism that merges population-level insights with personalized adaptations. Moreover, the integration of closed-loop optimization through reinforcement learning and digital twins facilitates customized interventions and feedback. In general, HSHI represents a significant shift in healthcare, moving towards a model that emphasizes prevention, adaptability, and a harmonious relationship between technology and health management.
Access Full Paper
This research paper is available on arXiv, an open-access archive for academic preprints.
Citation
Jingyi Zhao. "Artificial Intelligence-driven Intelligent Wearable Systems: A full-stack Integration from Material Design to Personalized Interaction." arXiv preprint. 2025-11-17. http://arxiv.org/abs/2511.13565v1
About arXiv
arXiv is a free distribution service and open-access archive for scholarly articles in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, systems science, and economics.
Disclaimer: This article is for informational purposes only and does not constitute financial advice. Consult a licensed financial advisor before making investment decisions.
Related Articles
Disclaimer
This article is for informational purposes only and does not constitute financial, legal, or tax advice. SwissFinanceAI is not a licensed financial services provider. Always consult a qualified professional before making financial decisions.
References
- [1]ResearchCredibility: 9/10Jingyi Zhao. "Artificial Intelligence-driven Intelligent Wearable Systems: A full-stack Integration from Material Design to Personalized Interaction." arXiv.org. November 17, 2025. Accessed November 18, 2025.
Transparency Notice: This article may contain AI-assisted content. All citations link to verified sources. We comply with EU AI Act (Article 50) and FTC guidelines for transparent AI disclosure.
Original Source
This article is based on Artificial Intelligence-driven Intelligent Wearable Systems: A full-stack Integration from Material Design to Personalized Interaction (arXiv.org)
