Biosensor Calibration via Production Data Tracking
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Solution Overview
Problem
Conventional blood glucose monitoring systems fail to provide real-time, personalized, and accurate analytics for diabetes management, lacking in rapid and reliable glucose concentration forecasting.
Innovation Solution
A biomonitoring and healthcare guidance system that uses biosensors with unique identifiers for individual calibration adjustments, generating production data during manufacturing to improve accuracy and adapt to patient-specific data, and employing machine learning for predictive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional blood glucose monitoring systems are used, then manufacturing costs are reduced and device simplicity is maintained, but measurement precision and reliability of glucose concentration forecasting deteriorate
Solution Approach 1:
The system performs preliminary calibration during the manufacturing process by measuring production data (electrode resistance, capacitor values, amplifier gains) and storing these values in memory. This preliminary action eliminates the need for complex post-manufacturing calibration procedures and ensures each sensor is pre-calibrated to its specific characteristics, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
Each biosensor automatically uses its own production data stored in memory to calibrate its measurements. The sensor performs self-calibration by applying correction factors derived from its manufacturing characteristics, eliminating the need for external calibration equipment or complex user procedures, thus improving measurement precision without increasing operational complexity.
2Measurement precision
If individual calibration adjustments are implemented for each biosensor, then measurement precision improves, but manufacturing time and process complexity increase
Solution Approach 1:
The calibration process is merged with the manufacturing process itself. Production data is collected during normal manufacturing operations, and calibration values are calculated and stored in the same production line without requiring separate calibration equipment or additional manufacturing steps. This integration maintains manufacturing throughput while achieving individual calibration for each sensor.
Solution Approach 2:
Manual or post-manufacturing calibration procedures are replaced with automated electronic measurement and calculation systems. Production data is automatically captured by measurement devices integrated into the manufacturing equipment, and calibration algorithms automatically process this data to generate correction factors, eliminating time-consuming manual calibration steps and maintaining high manufacturing productivity.
3Reliability
If production data is tracked and stored for each biosensor component, then quality control and measurement accuracy improve, but data management complexity and storage requirements increase
Solution Approach 1:
Only the essential production data parameters directly affecting sensor performance (electrode resistance, capacitor values, amplifier gains) are extracted and stored in memory. Non-essential manufacturing data is excluded, maintaining quality control reliability by capturing all necessary calibration parameters while minimizing data management complexity through selective data extraction.
Solution Approach 2:
The system transforms raw production data into calibrated measurement parameters. Production measurements are converted into correction factors and calibration coefficients that are stored in a compact format, changing the parameter representation from raw manufacturing measurements to optimized calibration values, thereby reducing storage requirements and simplifying data management while maintaining quality control.
Data Source
AI summary
Systems and methods for improving production and/or calibration of biosensors are disclosed herein. The biosensors can be used for personal biomonitoring and providing personalized healthcare assessments. The manufacturing method can include gathering production data throughout production of the biosensors and using the production data to predict performance metrics for each biosensor. The predicted performance metrics can be generated using one or more models correlating the production data to the performance metrics. The predicted performance metrics can then be used by a biomonitoring system to adjust operating parameters of the biosensor before a user relies on the healthcare assessments from the biomonitoring system.