Digital Twin-Based Multimodal Data Fusion for Health Monitoring in Smart Elderly Care Platforms
Abstract
In the smart elderly care mobile service platform, multimodal sensor data is redundant due to differences in format, accuracy, etc., and the lack of effective analysis makes it difficult to extract feature components, which affects the fusion effect. A new method is proposed for this: first, real-time health data is collected through platform integrated sensors such as electrocardiograms, and then the raw data is redundantly processed to generate standardized datasets in a unified format. Next, a digital twin model is constructed, and feature component extraction techniques are used to analyze the data distribution structure. The dynamic correlation of the quantified data is calculated by associating distribution features to complete the fusion. The experiments have proved that under the application of this method, when the elderly are in a state of hypertension, the blood pressure fusion value significantly increases to nearly 1. When the heart rate is too fast, the pulse fusion value and electrocardiogram fusion value reach approximately 0.8 and 0.7 respectively. The average accuracy rate of identifying various abnormal health conditions is over 85%. The overall standard deviation is 0.004, indicating that the degree of dispersion of the fused data is smaller. In terms of data fusion error, it is below 0.15% under all data volume conditions, which is conducive to promoting the development of the elderly care industry.References
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