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A Multi-agent System Approach with Generative AI for Improved Elderly Daily Living

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Progress in Artificial Intelligence (EPIA 2024)

Abstract

As the world population grows older, the demographic issue of aging becomes more prevalent in society, as this group starts facing increasing physical and psychological challenges throughout their daily lives. Older adults often experience a decline in mobility and increased distance from others, leading to the problem of social isolation can lead to anxiety and depression. Leveraging current technological advancements is fundamental in addressing these problems. In particular, the latest developments in generative AI offer significant potential for creating solutions adapted to elderly individuals. Generative AI can enable the development of systems that socially interact with older adults and monitor their behavior, potentially alleviating age-related issues. In the context of the RM4Health project, this paper proposes a novel architectural system that combines established technologies, such as multi-agent systems, with emerging generative AI and large language models. This integrated approach aims to provide an interactive environment for elderly users and real-time health monitoring through wearable devices to evaluate their well-being.

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Notes

  1. 1.

    https://zenodo.org/records/53894#.YMoUpnVKiP9.

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Acknowledgments

This research work was developed under the project RM4Health (ITEA-2021-21022-RM4Health), funded by the European Regional Development Fund (ERDF) within the project number COMPETE2030-FEDER-00391100, and funded by National Funds through the Portuguese FCT — Fundação para a Ciência e a Tecnologia under the R&D Units Project Scope, UIDB/00760/2020 (https://doi.org/10.54499/UIDB/00760/2020).

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Crista, V., Martinho, D., Marreiros, G. (2025). A Multi-agent System Approach with Generative AI for Improved Elderly Daily Living. In: Santos, M.F., Machado, J., Novais, P., Cortez, P., Moreira, P.M. (eds) Progress in Artificial Intelligence. EPIA 2024. Lecture Notes in Computer Science(), vol 14967. Springer, Cham. https://doi.org/10.1007/978-3-031-73497-7_11

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  • DOI: https://doi.org/10.1007/978-3-031-73497-7_11

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