Internet of Things in Society 5.0 and the Democratization of Healthcare

Demetriou, Demetra; Mathabe, Kgomotso; Lolas, Georgios; Dlamini, Zodwa

doi:10.1007/978-3-031-36461-7_5

Society 5.0 and Next Generation Healthcare 2023

DOI: 10.1007/978-3-031-36461-7_5

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Internet of Things in Society 5.0 and the Democratization of Healthcare

Demetra Demetriou,

Kgomotso Mathabe,

Georgios Lolas

et al.

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2024

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Cited by 2 publications

References 72 publications

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Human digital twins unlocking Society 5.0? Approaches, emerging risks and disruptions

Fontes,

Carpentras,

Mahajan

2024

Ethics Inf Technol

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Industry 5.0 and Healthcare 5.0 converge towards a human centered society, having technological advancement as a lever. In Society 5.0, decentralized autonomous cities and a convergence of physical and cyberspace are the foundations of the new chapter of society’s development. The idea of creating digital replicas and legitimate representatives of human beings in cyberspace has become a pillar of digitalization. Society 5.0 introduces Human Digital Twins as a central element of Cyber Physical Systems that include human factors or are designed to interact with humans in a personalized fashion. Overall, the HDT and neighboring concepts are applied to depict how humans can be represented in a cyberspace. However, there are clear challenges in determining which human characteristics should take precedence, how much autonomy should be granted to HDTs to optimize their functionality and how to conceptualize the digital environment in which HDTs interact with various entities, including other digital agents and stakeholders with agency and decisional power. To harness similarities and differences of current approaches, we propose a classification of HDTs based on meta-characteristics and ethical implications. We discuss ethical implication by focusing on emerging risks and paradigm shifts and anchor the previous discussion in the vision for Society 5.0, questioning whether societal development relying on disruptive technologies, instead of leading to more human-centered societies might be driving humanized societies away from humanity.

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Human digital twins unlocking Society 5.0? Approaches, emerging risks and disruptions

Fontes,

Carpentras,

Mahajan

2024

Ethics Inf Technol

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MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning

Sartaj,

Ali,

Gjøby

2024

ACM Trans. Softw. Eng. Methodol.

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Testing healthcare Internet of Things (IoT) applications at system and integration levels necessitates integrating numerous medical devices. Challenges of incorporating medical devices are: (i) their continuous evolution, making it infeasible to include all device variants, and (ii) rigorous testing at scale requires multiple devices and their variants, which is time-intensive, costly, and impractical. Our collaborator, Oslo City’s health department, faced these challenges in developing automated test infrastructure, which our research aims to address. In this context, we propose a meta-learning-based approach (MeDeT) to generate digital twins (DTs) of medical devices and adapt DTs to evolving devices. We evaluate MeDeT in Oslo City’s context using five widely-used medical devices integrated with a real-world healthcare IoT application. Our evaluation assesses MeDeT’s ability to generate and adapt DTs across various devices and versions using different few-shot methods, the fidelity of these DTs, the scalability of operating 1000 DTs concurrently, and the associated time costs. Results show that MeDeT can generate DTs with over 96% fidelity, adapt DTs to different devices and newer versions with reduced time cost (around one minute), and operate 1000 DTs in a scalable manner while maintaining the fidelity level, thus serving in place of physical devices for testing.

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Internet of Things in Society 5.0 and the Democratization of Healthcare

Cited by 2 publications

References 72 publications

Human digital twins unlocking Society 5.0? Approaches, emerging risks and disruptions

Human digital twins unlocking Society 5.0? Approaches, emerging risks and disruptions

MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning

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