A personalized heart rate abnormality analysis early warning method, system, device and medium

CN122392916APending Publication Date: 2026-07-14BEIJING XUEYANG TECH CO LTD

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Applications(China)
Current Assignee / Owner: BEIJING XUEYANG TECH CO LTD
Filing Date: 2026-03-21
Publication Date: 2026-07-14

AI Technical Summary

Technical Problem

Existing heart rate monitoring methods based on PPG sensors cannot account for individual differences among different users, resulting in inaccurate test results.

Method used

The system collects user physiological data using a multi-channel PPG sensor and combines it with environmental and status information to generate adjustment coefficients to adjust the initial physiological data. Based on the user's historical physiological data, a personalized health model is constructed and optimized by combining basic information and lifestyle data to generate a target health model. Finally, the system calculates the abnormal heart rate index and generates early warning information.

Benefits of technology

It improves the accuracy of heart rate abnormality detection, ensures personalized judgment criteria, eliminates the influence of environmental and state changes on physiological data, and generates more accurate target physiological data.

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Abstract

The application provides a personalized heart rate anomaly analysis early warning method, system, device and medium, relates to the technical field of health monitoring, and the method comprises the following steps: acquiring initial physiological data of a user; acquiring environment information and state information of an environment in which the user is located, combining the environment information and the state information to generate an adjustment coefficient, adjusting the initial physiological data according to the adjustment coefficient to generate target physiological data; constructing an initial health model of the user according to historical physiological data of the user; acquiring basic information of the user and lifestyle data of the user, combining the basic information and the lifestyle data to adjust the initial health model to generate a target health model; substituting the target physiological data into the target health model to generate a heart rate anomaly index of the user, and generating early warning information according to the heart rate anomaly index. The application has the technical effect that the accuracy of a detection result is improved.

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