Insulin Delivery Controller Event Classification
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Solution Overview
Problem
Current insulin delivery systems lack the ability to dynamically adjust insulin dosages based on real-time user activities and events, such as meals, exercise, and sleep patterns, leading to suboptimal glucose control.
Innovation Solution
A system that uses a database to classify detected events and generate controller tuning instructions to adjust insulin delivery, either increasing, decreasing, or maintaining insulin levels based on probability thresholds associated with specific user activities, utilizing a gesture-based event detection system and analyte sensors to monitor glucose levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If insulin delivery is adjusted dynamically based on detected events, then glucose control accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the control function by separating event detection (performed by external sensors and processors) from insulin delivery control (performed by the pump). The controller receives processed event data and adjusts insulin delivery based on pre-defined rules, dividing the complex task into manageable components that reduce overall system complexity while maintaining accuracy.
Solution Approach 2:
A controller acts as an intermediary between the event detection system and the insulin delivery mechanism. The controller processes detected events, applies control logic, and generates appropriate insulin delivery commands, serving as a mediator that simplifies the interaction between sensors and the pump while enabling accurate glucose control.
2Adaptability or versatility
If real-time event detection and response is implemented, then insulin dosing adaptability is improved, but loss of time for processing increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and classifying events before they reach the controller. Event detection, filtering, and initial interpretation are completed in advance, allowing the controller to quickly respond with minimal processing delay when events occur, thus maintaining high adaptability while reducing real-time processing time.
Solution Approach 2:
The system implements feedback mechanisms where glucose level measurements and event detection results are continuously fed back to the controller, which adjusts insulin delivery in real-time. This closed-loop feedback enables rapid adaptation to changing conditions while optimizing response time through efficient feedback processing.
3Loss of information
If multiple sensors and event detection mechanisms are added, then information completeness is improved, but device complexity increases
Solution Approach 1:
The system employs multi-functional sensors and detection mechanisms that can identify multiple types of events (meals, exercise, stress) using a unified detection framework. This universal approach allows comprehensive information collection about user activities while avoiding the complexity of separate specialized systems for each event type.
Solution Approach 2:
Multiple event detection functions are merged into an integrated event processing system that consolidates data from various sensors and sources. By combining detection, classification, and interpretation functions into a unified processing pipeline, the system achieves complete information gathering while reducing the complexity that would arise from multiple independent systems.
Data Source
AI summary
Methods, systems and computer-readable media are provided for controlling insulin delivery to a specific user by a medical device based on detected events associated with the specific user. An event combination or combination of the detected events can be determined and a database of classified outputs corresponding to event combinations can be accessed. A classified output corresponding to the event combination can be retrieved. A controller of the medical device can generate, based on the classified output, controller tuning instructions that influence delivery of insulin via the medical device to either decrease the insulin delivery to the specific user, increase the insulin delivery to the specific user, or maintain the insulin delivery to the specific user at the ordinary level by maintaining the insulin delivery in a nominal state.