Handheld Diabetes Device Pattern Recognition for Glucose Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Handheld diabetes management devices have limited user interfaces, making it difficult to configure reminders for blood glucose measurements, classify measurements, and identify trends in glucose levels effectively.
Innovation Solution
A handheld diabetes management device equipped with a blood glucose measurement engine, a clock, a processor, and memory that classifies blood samples into types like fasting, pre-breakfast, post-breakfast, etc., calculates evaluation parameters, and generates indicators for high or low glucose levels, displaying recognized patterns and prioritizing them for user awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the user interface of handheld diabetes management devices is simplified to reduce operational complexity, then ease of operation is improved, but the ability to configure reminders, classify measurements, and identify trends is worsened
Solution Approach 1:
The device automatically classifies blood glucose measurements into contextual categories (fasting, pre-meal, post-meal, bedtime) without requiring manual user input. The system self-configures reminder schedules based on detected patterns in measurement data, and automatically identifies trends without complex user configuration, thereby maintaining simplicity while enhancing adaptability
Solution Approach 2:
The device performs preliminary analysis of blood glucose measurements to pre-configure appropriate reminder schedules and classification parameters before the user needs them. By proactively analyzing measurement patterns and pre-setting appropriate monitoring parameters, the system eliminates the need for complex user configuration while maintaining high adaptability to individual needs
2Adaptability or versatility
If automated classification and pattern recognition features are added to handheld diabetes management devices, then adaptability is improved, but device complexity is worsened
Solution Approach 1:
The device segments blood glucose measurements into distinct contextual categories (fasting, pre-breakfast, post-breakfast, pre-lunch, post-lunch, pre-dinner, post-dinner, bedtime) based on timing patterns. This segmentation allows complex adaptability to be achieved through simple temporal classification rules rather than requiring complex algorithmic analysis, thereby maintaining device simplicity while enhancing functional adaptability
Solution Approach 2:
The device uses parameter changes in measurement timing and frequency to automatically adjust classification and trigger pattern recognition. By monitoring changes in when measurements are taken and how often they occur, the system adapts its behavior based on detected patterns without requiring complex user configuration or sophisticated algorithms, thus achieving high adaptability with minimal added complexity
3Measurement precision
If multiple blood glucose measurement classifications are implemented, then measurement precision is improved, but difficulty of detecting and measuring is worsened
Solution Approach 1:
The device automatically determines the contextual classification of each blood glucose measurement based on its timing relative to typical daily routines (meals, bedtime). The system self-configures appropriate classification parameters and automatically assigns categories without requiring user judgment or complex input, thereby achieving precise contextual measurement while maintaining ease of operation
Solution Approach 2:
The device performs preliminary classification of measurements into contextual categories based on timing patterns before the user needs to interpret the data. By pre-processing measurements and automatically assigning appropriate classifications, the system eliminates the need for users to manually determine measurement context, thus achieving high measurement precision without increasing user burden
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
A method includes: measuring a blood glucose (bG) level in a blood sample; storing the bG level and a time of receipt of the blood sample; storing a classification of the blood sample; in response to the receipt of the blood sample, selecting a group of stored bG levels having the classification of the blood sample and that were received within a predetermined period before receipt of the blood sample; calculating a bG evaluation parameter from the selected bG levels; evaluating the bG evaluation parameter in relation to first predetermined criteria, the first predetermined criteria including a first threshold indicative of a high bG level or a low bG level; selectively displaying an indication of recognition of a pattern in the selected bG levels when the bG evaluation parameter is greater than or less than the first threshold; and selectively removing the indication from the display.