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Summary:
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Recent advances in artificial intelligence (AI), the Internet of Things (IoT), digital health technologies, and connected healthcare systems have accelerated the development of intelligent health and well-being services, encompassing continuous health monitoring, personalised assistance, and proactive support. Large volumes of data from user activities, physiological measurements, environmental conditions, and service interactions can be analysed to support contextual understanding, health risk identification, adaptive interventions, and coordinated care services. Realising these capabilities, however, requires interoperable coordination across heterogeneous devices and service platforms, together with edge intelligence that supports real-time responsiveness, contextual understanding, and efficient collaboration with external AI services.
To support these needs, an AI-enabled edge platform can perform local data processing, context-aware analysis, lightweight AI inference, event detection, and intelligent orchestration of health and well-being services. It can also collaborate with advanced AI services and external healthcare and care systems to support long-term analysis, personalised recommendations, predictive health assessment, and coordinated service delivery, while protecting sensitive personal information.
This draft Recommendation specifies a functional framework for an AI-enabled edge platform supporting proactive health and well-being services in connected home and community environments. It describes functional capabilities, architectural considerations, intelligent service orchestration mechanisms, context-aware health monitoring functions, interactions with external healthcare and care systems, and representative use cases supporting proactive, personalised, and trustworthy health and well-being services.
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