Insulin Patch Control via Neural Network Simulation
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Solution Overview
Problem
Current insulin patch technologies face challenges such as high costs, difficulties in continuous data management, and real-time blood sugar management, with risks associated with independent blood sugar control, necessitating a more accurate and stable control method.
Innovation Solution
An insulin patch control method utilizing an artificial neural network to estimate pump parameters based on blood sugar information, enabling independent insulin injection decisions and real-time adjustments through communication with an insulin patch and external servers for enhanced accuracy and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a smart insulin patch is used for independent blood sugar control, then automation and convenience are improved, but safety and reliability deteriorate due to control risks
Solution Approach 1:
The patent implements a feedback mechanism where the insulin patch continuously monitors blood sugar levels and communicates with an external server or smartphone. The system receives feedback signals about blood sugar effectiveness and simulation results, then adjusts insulin delivery accordingly. This closed-loop feedback system enables automated control while maintaining safety through continuous monitoring and adjustment based on actual physiological responses.
2Measurement precision
If continuous blood sugar monitoring and neural network processing are implemented, then measurement precision and control accuracy are improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a smartphone or external server as an intermediary device that handles the complex neural network processing and data analysis. The insulin patch itself remains relatively simple, containing mainly the blood sugar sensor and insulin delivery mechanism. The intermediary device performs the computationally intensive tasks of neural network inference, simulation, and control algorithm execution, thereby achieving high measurement precision and control accuracy without making the wearable patch overly complex or expensive.
3Stability of the object's composition
If real-time blood sugar management and simulation are performed, then control stability is improved, but loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary actions by pre-training neural networks and pre-computing control strategies offline before real-time deployment. The system uses historical blood sugar data to train the neural network in advance, and performs simulation studies beforehand to optimize control parameters. During real-time operation, the pre-trained model can quickly process incoming blood sugar measurements and generate control decisions, thereby maintaining control stability while minimizing real-time data processing time.
Data Source
AI summary
Provided are an insulin patch control method and apparatus. An insulin patch control method according to an embodiment of the present disclosure includes obtaining blood sugar information measured by a blood sugar sensor, from the insulin patch, inputting the obtained blood sugar information to a first neural network, estimating a pump parameter of the insulin patch based on output data from the first neural network, and performing a simulation of the insulin patch by inputting the blood sugar information and the pump parameter into a second neural network.