Artificial Intelligence-driven Intelligent Wearable Systems: A full-stack Integration from Material Design to Personalized Interaction
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912715185848320 |
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| author | Zhao, Jingyi Shi, Daqian Wang, Zhengda Tang, Xiongfeng Qin, Yanguo |
| author_facet | Zhao, Jingyi Shi, Daqian Wang, Zhengda Tang, Xiongfeng Qin, Yanguo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13565 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence-driven Intelligent Wearable Systems: A full-stack Integration from Material Design to Personalized Interaction Zhao, Jingyi Shi, Daqian Wang, Zhengda Tang, Xiongfeng Qin, Yanguo Artificial Intelligence 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. |
| title | Artificial Intelligence-driven Intelligent Wearable Systems: A full-stack Integration from Material Design to Personalized Interaction |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.13565 |