Threshold-Based Glucose Monitoring for Power-Aware Insulin Control
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Solution Overview
Problem
Existing diabetes management systems using continuous glucose monitors (CGMs) with fixed time intervals are reactive and may miss significant glucose changes due to missed readings, leading to inaccurate insulin delivery and increased risk of hypo- or hyper-glycemic events.
Innovation Solution
A wearable drug delivery device with a processor that adjusts the frequency of glucose readings based on the rate of change, allowing for more frequent sampling during rapid glucose changes and enabling quicker insulin delivery adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If blood glucose readings are processed more frequently, then the response speed and accuracy of insulin delivery is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts the reading frequency based on the current glucose trend. When the rate of change exceeds a threshold, the system increases reading frequency to capture rapid changes. When glucose levels are stable, the system reduces frequency to conserve power. This dynamic adaptation resolves the contradiction between response speed and power consumption.
Solution Approach 2:
The system changes the time interval parameter between readings based on detected glucose trends. The processor monitors the rate of change and adjusts the sampling interval accordingly - shorter intervals during rapid changes, longer intervals during stability. This parameter adjustment allows the system to optimize both response speed and power consumption under different conditions.
2Use of energy by moving object
If fixed time interval readings are used, then power consumption is reduced, but the system becomes less responsive to rapid glucose changes
Solution Approach 1:
The system uses feedback from glucose trend analysis to adjust reading frequency. The processor continuously monitors glucose changes and feeds this information back to the sampling controller. When rapid changes are detected, the system increases sampling rate to maintain reliability. This feedback mechanism ensures responsiveness is maintained only when necessary, reducing unnecessary power consumption during stable periods.
Solution Approach 2:
The system transitions from a static fixed-interval approach to a dynamic adaptive approach. The reading interval becomes a variable parameter that changes based on glucose trend conditions. This dynamic behavior allows the system to maintain high reliability during rapid glucose changes while conserving power during stable periods, resolving the contradiction between power consumption and responsiveness.
3Use of energy by moving object
If larger time intervals are used between readings, then power consumption is reduced, but the gap in glucose awareness increases when readings are missed
Solution Approach 1:
The system adjusts the time interval parameter dynamically based on glucose stability. When glucose levels are stable, larger intervals are used to conserve power. When rapid changes are detected, the system reduces the interval to minimize information loss gaps. This adaptive parameter change ensures that power consumption is optimized without sacrificing glucose awareness during critical periods.
Solution Approach 2:
The system detects rapid glucose changes beforehand and proactively increases reading frequency to cushion against potential missed readings. By anticipating rapid changes through trend analysis, the system prepares by taking more frequent measurements, ensuring that even if a reading is missed, the glucose awareness gap remains minimal. This preparatory action protects against information loss while maintaining power efficiency during stable periods.
Data Source
AI summary
Provided is a wearable medical device that includes a processor or logic circuitry. The wearable medical device may include a memory storing instructions that, when executed by the processor or logic circuitry, configure the wearable medical device to determine, by the processor or the logic circuitry, that an event affecting a blood glucose measurement value trend of a user has occurred. Based on the occurrence of the event, the processor or the logic circuitry may select a mode of operation of the analyte sensor, and generate a signal indicating the selected mode of operation. The mode of operation may correspond to a sampling frequency of a physical attribute or physiological condition of a user of the wearable medical device.