Blood glucose monitoring method and electronic device
Through machine learning models and physiological data analysis, the duration and threshold of hypoglycemia warnings are dynamically adjusted, solving the problem of inaccurate warnings caused by individual differences in blood glucose monitoring devices and improving the accuracy of hypoglycemia warnings and user experience.
WO2025209494A1 Publication Date: 2025-10-09HUAWEI TECH CO LTD
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Patent Information
- Application Number
- PCT/CN2025/086716
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2024-04-03
- Filing Date
- 2025-04-01
- Publication Date
- 2025-10-09
AI Technical Summary
Technical Problem
In the existing technology, blood glucose monitoring equipment uses fixed hypoglycemia warning duration and threshold, which cannot adapt to the individual differences of different populations, resulting in overly sensitive or too slow warnings, affecting the user experience.
Method used
Through machine learning models, the hypoglycemia warning duration and threshold are dynamically adjusted based on the user's historical blood sugar data and physiological data, and the warning strategy is optimized in combination with the user's activity status.
Benefits of technology
It improves the accuracy of hypoglycemia warning and user experience, and avoids the impact of hypoglycemia on daily life and work.
✦ Generated by Eureka AI based on patent content.
Abstract
The present application provides a blood glucose monitoring method. The method comprises: determining a first early warning duration according to first blood glucose data, wherein the first blood glucose data comprises blood glucose data within a first time period, and the first early warning duration is used for performing hypoglycemia early warning. The first early warning duration is related to blood glucose fluctuation indicated by the first blood glucose data. If the blood glucose indicated by the first blood glucose data fluctuates little and changes slowly, the first early warning duration can be longer. If the first blood glucose data input into a model indicates that the blood glucose of a user fluctuates greatly and changes rapidly in historical time, the first early warning duration can be shorter. By means of implementing the described method, the electronic device can adaptively adjust the first early warning duration according to the characteristics of the blood glucose variability of a user, thereby improving the accuracy of hypoglycemia early warning.
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