An Intelligent and Privacy-Preserving Digital Twin Model for Aging-in-Place

2025-04-04

AI Summary

Conventional home monitoring systems for aging-in-place exhibit high intrusiveness, insufficient privacy protection, and poor environmental adaptability, particularly when handling sensitive health data. Method: This work proposes a lightweight, interpretable, privacy-preserving digital twin system integrating multimodal low-power sensors, edge-deployed lightweight neural networks, and symbolic decision rules to enable unobtrusive behavioral sensing, anonymized feature extraction, and fine-grained activity recognition entirely on-device. Contribution/Results: The system introduces the first digital twin framework explicitly balancing privacy preservation and modeling fidelity, supporting personalized health interventions. Evaluated over two months in two real households, it demonstrates sustainable, energy-efficient, and secure operation—achieving significantly higher accuracy in daily activity and anomaly detection than baseline methods, thereby providing a robust technical foundation for privacy-aware home-based health intervention.

Technology Category

• Machine Learning: Privacy
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Privacy-enhancing technologies

Abstract

The population of older adults is steadily increasing, with a strong preference for aging-in-place rather than moving to care facilities. Consequently, supporting this growing demographic has become a significant global challenge. However, facilitating successful aging-in-place is challenging, requiring consideration of multiple factors such as data privacy, health status monitoring, and living environments to improve health outcomes. In this paper, we propose an unobtrusive sensor system designed for installation in older adults' homes. Using data from the sensors, our system constructs a digital twin, a virtual representation of events and activities that occurred in the home. The system uses neural network models and decision rules to capture residents' activities and living environments. This digital twin enables continuous health monitoring by providing actionable insights into residents' well-being. Our system is designed to be low-cost and privacy-preserving, with the aim of providing green and safe monitoring for the health of older adults. We have successfully deployed our system in two homes over a time period of two months, and our findings demonstrate the feasibility and effectiveness of digital twin technology in supporting independent living for older adults. This study highlights that our system could revolutionize elder care by enabling personalized interventions, such as lifestyle adjustments, medical treatments, or modifications to the residential environment, to enhance health outcomes.

Problem

• Developing privacy-preserving digital twin for aging-in-place
• Monitoring health and living environment unobtrusively
• Enabling personalized elder care interventions cost-effectively

Innovation

• Unobtrusive sensor system for elder homes
• Neural network models for activity tracking
• Privacy-preserving digital twin technology