Adaptive Insulin Controller Using Bio-Signals and Lifelog Data
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Solution Overview
Problem
Current blood glucose control algorithms for artificial pancreas systems do not adequately consider individual lifestyle and mental health factors, leading to limitations in responding to changes in blood glucose levels.
Innovation Solution
A blood glucose control system that includes a continuous glucose meter, bio-signal sensor, lifelog data collector, and insulin injection controller, which determines insulin injection amounts and rates based on blood glucose measurements, bio-signals, stress levels, and lifelog data to provide personalized insulin delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a static control algorithm is used, then the device complexity is reduced, but the adaptability to individual physiological characteristics and lifestyle changes deteriorates
Solution Approach 1:
The control algorithm transitions from a static model to a dynamic adaptive model that continuously learns and updates based on patient-specific physiological characteristics, lifestyle patterns, and real-time glucose data. The system adapts its parameters over time to reflect changing individual conditions, thereby resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
The system performs self-learning by automatically analyzing patient data patterns, physiological characteristics, and lifestyle information to continuously optimize control parameters without requiring manual reconfiguration. This self-adaptive capability enables the algorithm to improve its performance autonomously while maintaining operational simplicity for the user.
2Adaptability or versatility
If an adaptive control algorithm that simulates physiological characteristics is used, then the adaptability improves, but the device complexity increases
Solution Approach 1:
The system implements continuous feedback loops where real-time glucose monitoring data, physiological measurements, and lifestyle information are fed back into the control algorithm. This feedback mechanism enables the system to dynamically adjust insulin delivery based on actual patient responses, improving physiological simulation while managing complexity through iterative optimization.
Solution Approach 2:
The system performs preliminary learning during an initial adaptation period to establish patient-specific physiological models and lifestyle patterns before full control activation. This preliminary action phase allows the complex adaptive algorithm to be pre-configured with individual characteristics, reducing the perceived complexity during ongoing operation.
3Measurement precision
If lifestyle and mental health data are collected and analyzed, then the blood glucose control precision improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The data processing system is segmented into modular functional components: data collection modules for different data types (glucose, physiological, lifestyle), data processing modules for pattern recognition and analysis, and control modules for insulin delivery decisions. This segmentation allows the complex system to be managed through independent, specialized modules that can be developed and maintained separately.
Solution Approach 2:
The system introduces intermediate processing layers including data filtering, feature extraction, and pattern recognition algorithms that bridge raw multi-source data and control decisions. These intermediary processing steps simplify the complexity by transforming diverse raw data into meaningful features that directly inform insulin delivery adjustments.
Data Source
AI summary
A blood glucose control system according to an embodiment of the present invention includes a continuous blood glucose meter configured to continuously measuring blood glucose of a patient, a bio-signal sensor configured to acquire a bio-signal of the patient, a lifelog data collector configured to collect lifelog data of the patient, an insulin injection controller configured to determine an insulin injection amount and an injection rate based on one or more of blood glucose measurement information and blood glucose metabolism characteristic information received from the continuous blood glucose meter, the bio-signal acquired by the bio-signal sensor or a stress level of the patient indexed by the bio-signal, and the lifelog data collected by the lifelog data collector, and an insulin pump configured to inject insulin into a body of the patient according to the insulin injection amount and the injection rate determined by the insulin injection controller.