Real-time Fall Monitoring System for the Elderly Based on Multimodal Sensor Fusion

With the acceleration of the global aging process, falls among the elderly have become a major public health issue threatening their health. Currently, the single sensor monitoring technology has significant limitations: the misjudgment rate of wearable accelerometers for daily activities, visual monitoring is significantly affected by light and there is a potential risk of privacy leakage, making it difficult to adapt to complex home scenarios. This paper reviews the research progress of real - time fall monitoring systems for the elderly based on multimodal sensor fusion, focuses on analyzing the collaborative mechanisms of millimeter - wave radar, accelerometers, and heart rate sensors, and summarizes key technologies such as data fusion architectures, algorithm optimization, and edge computing deployment. By comparing the performance differences of different fusion strategies, it is found that the three - level attention fusion architecture performs best in complex scenarios. At the same time, this paper points out problems in current research, such as the insufficient proportion of open - source data in home scenarios and the lack of night - time monitoring solutions, and looks forward to the future development direction of combining the Transformer architecture with privacy computing.