Introduction: With the global aging population, the demand for intelligent systems in elderly care and rehabilitation has surged. Artificial Intelligence (AI) has revolutionized this field through solutions like remote health monitoring, early disease detection, and assistive robots. This paper explores the latest AI-driven technologies in elderly care and rehabilitation
Methods: Data Collection: Wearable sensors (e.g., smartwatches) and surveillance cameras to track vital signs and daily activities.
Algorithms: Machine learning (ML) and deep neural networks (CNN, RNN) for medical data analysis and risk prediction (e.g., falls).
Robotics: Social and rehabilitation robots to assist physical mobility and social interactions.
Evaluation: Comparison with traditional methods (e.g., human nursing) via accuracy, speed, and user satisfaction metrics
Results: AI systems achieved 92% accuracy in dementia detection and 87% accuracy in fall prediction, outperforming manual methods.
Assistive robots (e.g., PARO) reduced loneliness by 40% in elderly users.
Challenges: Ethical concerns (privacy) and dependency on technological infrastructure.
Conclusion: AI holds significant potential to enhance elderly quality of life and reduce care costs. However, integrating this technology into existing healthcare systems and addressing ethical challenges require interdisciplinary collaboration