An Intelligent and Privacy-Preserving Digital Twin Model for Aging-in-Place
Journal:
arXiv
Published Date:
Apr 4, 2025
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.
