Analyte Sensor Battery Life Prediction for Reliable Glucose Monitoring
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Solution Overview
Problem
Conventional methods for monitoring blood glucose levels in diabetic patients are inconvenient and infrequent, leading to delayed detection of hyperglycemic or hypoglycemic conditions, and existing continuous glucose monitoring devices lack effective battery life management, resulting in unpredictable device performance.
Innovation Solution
A method and system for monitoring battery life in analyte sensors, including measuring current power levels, predicting remaining battery life, and adjusting operations based on usage patterns, with features like voltage drop analysis and Coulomb counting to extend battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If continuous glucose monitoring is implemented, then monitoring frequency and timeliness are improved, but power consumption increases and battery life decreases
Solution Approach 1:
The system implements periodic data transmission instead of continuous transmission, where glucose data is sent at predetermined time intervals. The controller alternates between active measurement periods and low-power sleep periods, enabling continuous monitoring while significantly reducing average power consumption to extend battery life.
Solution Approach 2:
The system dynamically adjusts its operational mode based on battery status and usage patterns. The controller can switch between different transmission frequencies, measurement intervals, and power management modes to optimize the balance between monitoring continuity and power conservation throughout the device's operational life.
2Reliability
If battery life prediction and management features are added, then device reliability is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary battery status assessment and remaining useful life prediction during manufacturing and initial setup. By pre-characterizing battery capacity and degradation rates, the system can predict future battery status without requiring complex real-time analysis, thus improving reliability while minimizing added complexity.
Solution Approach 2:
The system implements feedback mechanisms that monitor actual power consumption and battery voltage over time, comparing measured values against predicted models. This feedback loop allows the system to refine its battery life predictions and adjust operational parameters dynamically, improving reliability through adaptive management rather than through complex hardware additions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the reliability of continuous glucose monitoring by accurately predicting battery life and optimizing power consumption, reducing the risk of device failure and improving user safety by ensuring timely alerts and data transmission.
Implementation Method 1
the measured value can be a voltage drop indicative of an internal resistance of the battery
Implementation Method 2
The analyte sensor system can include a battery measurement module configured to measure a value indicative of a current power level of the battery and a prediction module configured to predict the remaining useful life of the battery based on the measured value and an assumed future usage of the analyte sensor system
Data Source
AI summary
Systems and methods for analyte monitoring, particularly systems and methods for monitoring and managing life of a battery in an analyte sensor system worn by a user, are provided.