Perspective Chapter: Enhancing Elderly Care through AI Approach
Yue Wang, Kaiyuan Yang, Tiantai Deng · Biomedical engineering · 2026
As global populations age, ensuring the safety and well-being of elderly individuals—particularly through reliable fall detection—has become a pressing public health concern. This chapter explores the intersection of electronic engineering, artificial intelligence (AI), and healthcare technologies to address the challenge of real-time fall detection. We provide a comprehensive overview of the evolution from conventional sensor-based systems to modern AI-driven solutions, including vision-based detection, pose estimation models such as MediaPipe and YOLO, and edge computing platforms for real-time processing. A robust fall detection algorithm is proposed, leveraging ensemble classifiers and temporal pose data, and is evaluated against public datasets. Extensive comparisons with existing approaches demonstrate the proposed model’s reliability, responsiveness, and computational efficiency. Through detailed discussion of system design, implementation, and deployment constraints, this chapter highlights the promise of AI in enhancing elderly care and enabling intelligent, decentralized health monitoring systems.