An Edge-Cloud Collaborative Architecture for Proactive Elderly Care: Real-Time Risk Assessment and Three-Level Emergency Response

• URL: http://arxiv.org/abs/2604.14154v1
• Date: Fri, 20 Mar 2026 03:00:12 GMT
• Title: An Edge-Cloud Collaborative Architecture for Proactive Elderly Care: Real-Time Risk Assessment and Three-Level Emergency Response
• Abstract summary: Existing cloud-centric platforms face critical limitations, including high latency unsuitable for emergency response.<n>This paper proposes an edge-cloud collaborative architecture that addresses these challenges through real-time multi-modal sensor fusion.<n>A three-level emergency response system coordinates responses among family members, community doctors, and nearby volunteers.
• Score: 2.2917707112773598
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid aging of global populations has created an urgent need for intelligent healthcare monitoring systems to ensure the safety of elderly individuals living independently. Existing cloud-centric platforms face critical limitations, including high latency unsuitable for emergency response, privacy risks from continuous transmission of sensitive data, and limited, single-channel alert mechanisms lacking scalability and context awareness. This paper proposes an edge-cloud collaborative architecture that addresses these challenges through real-time multi-modal sensor fusion, a four-dimensional risk assessment model, and a three-level emergency response system. The framework adopts a five-layer design - device, edge, service, data, and application layers - enabling real-time risk evaluation with end-to-end alert latency under three seconds. At the edge, a weighted multi-modal fusion algorithm integrates data from five sensor types with confidence propagation. A unified risk score is generated by combining fall probability, physiological indicators, behavioral patterns, and sensor anomaly metrics. Based on dynamic thresholds, a three-tier notification system coordinates responses among family members, community doctors, and nearby volunteers. Experiments on CASAS, MIMIC-III, and SisFall datasets show that the approach achieves 91% activity recognition accuracy and an 84% anomaly detection F1-score, outperforming single-sensor methods. Deployment on Raspberry Pi 4 gateways demonstrates sub-100 ms inference latency while preserving privacy by keeping raw data local. This architecture advances practical, privacy-preserving, and responsive elderly care systems.

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