AI Glucose Spike Prediction System Using Continuous Monitoring
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Solution Overview
Problem
Individuals with pre-diabetes and diabetes face significant health risks due to mindless eating, which leads to repeated blood sugar spikes, insulin resistance, and various complications such as heart attacks, strokes, kidney damage, nerve damage, and increased risk of infections and mortality.
Innovation Solution
A system comprising a continuous glucose monitoring device, an artificial intelligence server, and a mobile application that provides real-time glucose level data and predictive AI warnings of likely food-induced glucose spikes, along with personalized recommendations to improve health and well-being.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous glucose monitoring and AI predictive warnings are implemented, then glucose management effectiveness is improved, but device complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: continuous glucose monitoring device for data collection, mobile application for processing and AI analysis, and separate recommendation engine. This segmentation allows each component to specialize in specific tasks, improving overall glucose management effectiveness while making the complex system more manageable and maintainable through modular architecture
Solution Approach 2:
The mobile application serves as an intermediary between the glucose monitoring device and the user, and between the AI server and the monitoring device. This intermediary layer handles data processing, communication protocols, and user interface interactions, reducing the complexity burden on individual components while maintaining system-wide effectiveness
2Measurement precision
If real-time glucose monitoring and AI analysis are performed continuously, then health warning accuracy is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic glucose monitoring at optimized intervals rather than truly continuous monitoring, with the AI algorithm analyzing patterns over time. The mobile application processes data in batches and uses predictive algorithms to identify trends, maintaining high warning accuracy while reducing the energy burden of constant real-time processing
Solution Approach 2:
The AI model performs preliminary analysis of glucose patterns and predicts future spikes before they occur. By identifying risk patterns in advance and providing preventive warnings, the system achieves high accuracy in health warnings while reducing the need for intensive real-time processing during critical moments, thereby lowering overall energy consumption
Data Source
AI summary
Disclosed are a system, device, and method for predictive Artificial Intelligence (AI) warnings of likely food-induced glucose spikes, according to one embodiment. An artificial intelligence powered health system includes a continuous glucose monitoring device to capture a real-time glucose level data associated with a body of a wearer. An artificial intelligence server provides a recommendation to improve a health and a well-being of the wearer. A mobile device to generate an alert when an input of a food item consumed by the wearer and/or a behavioral activity performed by the wearer exhibits a health parameter which is greater than a predefined threshold based on a real time health information captured by the continuous glucose monitoring device. A mobile application to recommend an action to the wearer to reduce the health parameter to a normal range, according to this aspect.