Elderly monitoring system based on multisensor information fusion

Traditional monitoring systems for older adults typically rely on a single type of sensor, resulting in limited data sources that limit the comprehensiveness and diversity of monitoring. This limitation makes it difficult for the system to effectively and comprehensively identify complex abnormal behaviors, thus failing to provide sufficient data support for decision-making. To address this problem, this study proposes a multi-sensor information fusion-based monitoring system for the elderly. The system employs an advanced fusion algorithm to fuse data from visible, infrared and other sensors. To further improve the accuracy and coverage of the system monitoring, this study introduces a fusion genetic wolf pack algorithm to optimize the sensor layout and ensure the optimal configuration of sensors in the monitoring environment. By combining multi-sensor information fusion with optimal layout, the system not only monitors the multidimensional living environment and physiological state of the elderly more accurately, and provides comprehensive and accurate monitoring of the elderly's daily activities and health status, but also provides a solid data base for the detection and processing of abnormal behaviors.