Research on the Application of Remote Health Monitoring System Based on Intelligent Sensing in Home-Based Elderly Care

This paper designs a real-time stream processing algorithm driven by intelligent sensing to meet the high requirements of real-time and accuracy in home-based elderly care scenarios. The system collects physiological and behavioral data by integrating multimodal sensors (such as heart rate, blood oxygen, motion sensors, etc.), and uses distributed stream processing architecture to realize real-time analysis and anomaly detection of data. Then an improved dynamic threshold adaptive algorithm is proposed, which significantly improves the detection accuracy of health events while reducing the false alarm rate. The performance and reliability of the system are verified based on real health data sets in model simulation. The experimental results show that the designed system achieves an accuracy of 98.5% in abnormal event detection, and the processing delay is controlled within 200 milliseconds, which is more than 30% better than the traditional algorithm. In addition, the system has good scalability and stability in multi-task concurrent scenarios. This remote health monitoring system can effectively improve the health management level of home-based elderly care and provide personalized and efficient health protection for the elderly.