Falls among the elderly are a serious public health issue, and timely identification and response to fall incidents can significantly reduce the health risks for elderly individuals. Existing fall detection technologies suffer from low accuracy and delayed response times, necessitating improvements. This study designed a fall detection system based on a dual-stream convolutional neural network (CNN). The system employs an improved dual-stream CNN model to efficiently identify fall behaviors among the elderly. An intelligent remote monitoring system was constructed to collect and process data in real-time within the elderly's living environment, analyzing behavior information to detect fall incidents. Experimental results show that the improved dual-stream CNN model achieves an accuracy of 96.5% in fall detection, significantly outperforming traditional algorithms. The designed remote intelligent monitoring system not only intelligently analyzes environmental information but also provides real-time health and safety alerts and monitoring. This study combines the dual-stream CNN and the remote intelligent monitoring system to achieve efficient fall behavior detection and real-time monitoring for the elderly. Additionally, optimized indoor designs, such as adjusting furniture layout, increasing storage space, improving ventilation, and implementing slip protection measures, further enhance the safety of the elderly's living environment
Shuangshuang Chen (Fri,) studied this question.