Glucose Analyzer Adaptive Reminder Algorithm
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Solution Overview
Problem
Existing glucose analysis instruments for diabetics face challenges in reminding users to perform blood glucose measurements at individually suitable times, particularly due to compact designs and limited input options, which can be inconvenient for users with reduced manual abilities and do not adapt to individual daily routines.
Innovation Solution
A glucose analysis instrument that stores event data related to user activities such as glucose measurements, food intake, and physical activity, using a processor to determine reminder times through a reminder time determination algorithm, allowing for flexible adaptation to the user's daily routine without requiring manual input of times, and utilizing event data over multiple days to generate personalized reminder signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If the instrument is designed to be compact and portable, then it can be taken along by diabetics at all times, but the operating elements such as keys and buttons must be provided in very small form and few in number, making inputting times difficult and inconvenient
Solution Approach 1:
The instrument automatically determines reminder times by analyzing stored event data without requiring manual user input. The system serves itself by collecting data about the user's daily routine and autonomously calculating optimal measurement times, eliminating the need for users to manually enter times into the compact device.
Solution Approach 2:
The system changes from requiring manual time parameters (user input) to automatically deriving time parameters from event data analysis. The processor analyzes patterns in stored events and dynamically determines reminder times based on learned user behavior, transforming the operation from manual parameter entry to automated parameter generation.
2Device complexity
If the instrument requires manual input of reminder times, then the design can be simple, but users with reduced manual abilities find it difficult and inconvenient to use
Solution Approach 1:
The instrument performs the function of determining reminder times automatically without requiring user interaction for time entry. The system collects event data, analyzes patterns, and self-determines optimal reminder times, making the operation accessible to users with reduced manual abilities.
Solution Approach 2:
The mechanical interaction of pressing keys to input times is replaced by an automated computational process. The processor analyzes event data and generates reminder times algorithmically, substituting manual mechanical input with automated electronic determination.
3Ease of operation
If fixed reminder times are used, then the instrument operation is simple, but it does not adapt to individual daily routines and changes in user behavior
Solution Approach 1:
The reminder times transition from being static and fixed to being dynamic and adaptive. The system continuously analyzes event data and adjusts reminder times based on detected patterns in user behavior, allowing the instrument to adapt to individual routines and lifestyle changes.
Solution Approach 2:
The system implements feedback by analyzing stored event data about user activities and using this information to determine and adjust reminder times. The processor continuously evaluates patterns in the data and modifies reminder schedules accordingly, creating a closed-loop adaptive system.
4Quantity of substance
If manual input of reminder times is required, then fewer data storage requirements are needed, but the instrument cannot provide personalized reminders based on user behavior patterns
Solution Approach 1:
The system performs preliminary data collection by storing event data about user activities before determining reminder times. This advance accumulation of behavioral data enables subsequent personalized determination of reminder times based on analyzed patterns.
Solution Approach 2:
The system uses feedback from stored event data to determine personalized reminder times. The processor analyzes patterns in the collected data and uses this information to generate customized reminder schedules that adapt to individual user behaviors.
Data Source
AI summary
Glucose analysis instrument for diabetics, comprising a measuring device for determining glucose concentration values, a displaying device for displaying glucose concentration values, a signaling device for generating a reminder signal, and a control and evaluation device that comprises a processor and a data memory and is used to determine reminder times at which the signaling device is actuated. Event data are stored in the data memory, the event data containing information on events occurring in the life of a user of the glucose analysis instrument and on the time of occurrence of such events. The reminder times are determined by means of a reminder time determination algorithm taking into consideration event data from at least one previous day.