Published December 6, 2025 | Version v1
Journal article Open

Artificial Intelligence-Driven Smart Homecare: A Review of Applications, Challenges, and Prospects

  • 1. Independent Researcher, New York, United States
  • 2. Department of Information Systems and Cyber Security, University of Texas at San Antonio, Texas, United States
  • 3. College of Communication , Emerging Media Studies(EMS), Boston University, Boston, United States
  • 4. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, United States
  • 5. School of Engineering, Computer & Mathematical Sciences (ECMS), Auckland University of Technology, Auckland, New Zealand

Description

With the accelerating global trend of population aging, traditional elderly care models are increasingly strained by limited service resources and growing quality demands. As a sustainable and cost-effective alternative, home-based elderly care has garnered widespread attention. However, older adults living at home often face critical challenges, including discontinuous health monitoring and insufficient emotional support. The rapid advancement of Artificial Intelligence (AI) technologies offers new pathways for developing intelligent elderly care solutions. This review systematically examines the major applications of AI in home-based care, focusing on two key domains: intelligent health monitoring and management and smart home environment systems. By analyzing representative research findings and practical implementations both domestically and internationally, this paper identifies core challenges across technical, user-centric, systemic, and ethical dimensions. Furthermore, it outlines future research directions, including personalized modeling, edge intelligence, empathetic human-AI interaction, multimodal data integration, and privacy-preserving strategies. This review aims to provide a theoretical foundation and a comprehensive framework to guide future research and practical advancements in AI-powered smart elderly care.

Files

251202.pdf

Files (129.0 kB)

Name Size Download all
md5:8963c852a8c2866373741f7539e0d011
129.0 kB Preview Download

Additional details