Research on the deep learning-based image recognition algorithm for elderly health status

With the rapid development of artificial intelligence technology, deep learning has shown tremendous potential in various fields, especially in elderly health monitoring. Combining deep learning with machine vision, intelligent monitoring systems provide innovative solutions for real-time and automated health status recognition. This paper proposes a deep learning-based elderly health status image recognition algorithm, which utilizes image data collected by video surveillance systems. Through deep convolutional neural networks (CNN), the system performs feature extraction and behavior recognition, successfully achieving automatic recognition of abnormal health behaviors such as falls, chest pain, and headaches in the elderly. Experimental verification shows that the algorithm performs efficiently and accurately in various test scenarios, maintaining high precision even in complex backgrounds. During the experiments, high-resolution optical instruments (such as HD cameras) were used to collect image data of the elderly's daily activities, ensuring the diversity and representativeness of the data. This study provides a feasible technical path for elderly health monitoring, especially for further applications in smart homes, smart healthcare, and health management systems, laying the foundation for intelligent health monitoring technologies.