Heart rate respiration prediction method, system, program product, device, and storage medium

Abstract

This application provides a method, program product, electronic device, and storage medium for predicting heart rate and respiratory rate. The method includes: acquiring the original signal sequence collected from the human body via ultra-wideband radar; determining the chest cavity displacement signal based on the mapping relationship between phase change and chest cavity displacement; extracting multi-domain features from the chest cavity displacement signal to obtain multi-domain features, including time-domain features, frequency-domain features, and time-frequency-domain features; and obtaining predicted values ​​based on the multi-domain features using a pre-trained prediction model, the predicted values ​​including heart rate and respiratory rate in the future time period. Multi-dimensional feature fusion enhances the robustness of the algorithm, maintaining high accuracy even under electromagnetic interference or multi-target scenarios, improving the reliability of heart rate and respiratory rate prediction in complex environments. The physical signal advantages of ultra-wideband radar complement the environmental adaptability of neural networks, improving the accuracy of heart rate and respiratory rate prediction.

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