Intermittent Sensor Data Prediction for Continuous Glucose Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Continuous glucose monitors (CGMs) struggle to provide continuous insights for users, especially when intermittent or sparse sensor data is available, as traditional systems rely on continuous data for accurate predictions and recommendations.
Innovation Solution
A computer system that acquires first sensor data from a CGM, determines if data is no longer being acquired, classifies it as intermittent, and uses this data to generate predicted analyte level data for subsequent time periods, even when the sensor is not actively collecting data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous sensor data is required for accurate predictions, then measurement precision is improved, but device complexity and user compliance worsen
Solution Approach 1:
The system collects and stores sensor data during periods when the sensor is active, preparing the data in advance for later prediction periods. This preliminary data collection and storage enables accurate predictions to be made later without requiring continuous active sensing, thus resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The system dynamically adapts its operation mode between active data collection and passive prediction based on whether the sensor is currently active or inactive. This dynamic approach allows the system to maintain high prediction accuracy when data is available while reducing complexity and power consumption during inactive periods
2Measurement precision
If continuous sensor data is required for accurate predictions, then measurement precision is improved, but loss of time worsens
Solution Approach 1:
The system performs preliminary data collection and model training during active periods, storing both the sensor data and pre-computed insights. This allows the system to provide accurate predictions during inactive periods without needing to collect data in real-time, thus eliminating the time loss associated with continuous data collection
3Ease of operation
If intermittent sensor data is used for predictions, then ease of operation is improved, but measurement precision worsens
Solution Approach 1:
The system implements feedback mechanisms that continuously evaluate the quality and quantity of available sensor data. When sufficient data is available, the system trains prediction models to high accuracy. When data is sparse, the system adapts by using alternative data sources or reducing prediction frequency, thereby maintaining acceptable precision while preserving ease of operation
Solution Approach 2:
The system dynamically changes operational parameters such as prediction confidence thresholds, data sampling rates, and model complexity based on the availability of intermittent sensor data. This allows the system to maintain reasonable prediction accuracy across varying data availability conditions while keeping the system easy to operate
Data Source
AI summary
Techniques for predicting analyte levels based on intermittent sensor data are disclosed. First sensor data is acquired from an analyte sensor. The first sensor data reflects analyte levels of a user who is wearing the analyte sensor. The first sensor data is collected over a first time period. Later, a determination is made as to whether data is still being acquired from the analyte sensor. As a result of determining that data is no longer being acquired from the analyte sensor, the first sensor data is classified as intermittent analyte data. The intermittent analyte data is then used to generate predicted analyte level data for the user during a second time period that is subsequent to the first time period. The predicted analyte level data is reflective of the intermittent analyte data.