关键词: YOLOv8-pose, 多模态, 居家安全监护系统, 树莓派

Abstract: With the aging population increasing in China, the number of elderly people living alone has risen, highlighting home safety concerns. A multimodal home safety monitoring system based on YOLOv8-pose was proposed. The system integrated hardware, software, and server components to achieve comprehensive monitoring and health management for elderly individuals through multimodal data collection, model detection, and big data analysis. Hardware based on Raspberry Pi 4B was used for data acquisition. A management platform was built using a front-end and back-end separated architecture. The server-side deployed a posture detection model trained on YOLOv8-pose, enabling accurate behavior recognition and real-time alerts. A dataset of daily activity patterns was established using big data analysis techniques. When abnormalities were detected, alerts were sent to family members and community staff via a mini-program and web interface in no time, addressing emergency response challenges. Experiments showed that the human pose recognition accuracy of the detection model reached as high as 96.3%. The system also demonstrated excellent performance in overall cost control, providing practical references for the improvement of China’s smart elderly care system.

Key words: YOLOv8-pose, multimodal, home safety monitoring system, Raspberry Pi