AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts
The proposed AI-driven monitoring system offers significant advancements over traditional methods by handling complex and uncertain environments, adapting to varying patient conditions influenced by stress and depression, and making autonomous, real-time decisions. While the framework demonstrates high accuracy and adaptability, challenges related to data scale and future vital sign prediction remain. Future research will focus on extending predictive capabilities to further enhance proactive healthcare interventions.
Authors
- Jianming Yong (ORCID: https://orcid.org/0000-0003-4111-1076)
- Haoran Xie (ORCID: https://orcid.org/0000-0003-0965-3617)
- Thanveer Shaik (ORCID: https://orcid.org/0000-0002-9730-665X)
- Xiaohui Tao (ORCID: https://orcid.org/0000-0002-0020-077X)
- Hong‐Ning Dai (ORCID: https://orcid.org/0000-0001-6165-4196)
- Feng Zhao (ORCID: https://orcid.org/0000-0001-9003-8173)
- Lin Li (ORCID: https://orcid.org/0000-0001-7553-6916)
Institutions
- Hong Kong Baptist University (HK)
- University of Southern Queensland (AU)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Brain Informatics
- Published
- 2025-06-09
- DOI
- https://doi.org/10.1186/s40708-025-00262-1
- Citations
- 8
- Primary Topic
- Mental Health Research Topics
- Type
- article
- Field-Weighted Citation Impact
- 16.95