Wearable Biosensor Calibration via Embedded Production Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional blood glucose monitoring systems fail to provide real-time, personalized, and accurate analytics for chronic health conditions like diabetes, lacking rapid and reliable glucose concentration forecasting.
Innovation Solution
The development of wearable biosensors with advanced manufacturing methods that include data-driven calibration adjustments and production data extrapolation, allowing for precise calibration and performance prediction, and dynamic manufacturing adjustments to enhance accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional blood glucose monitoring systems are used, then basic glucose measurement is provided, but real-time analytics, personalized analytics, and glucose concentration forecasting are not provided accurately or rapidly
Solution Approach 1:
The system performs preliminary calibration by manufacturing biosensors with embedded production data (temperature, humidity, pressure, material composition) and pre-calculated calibration parameters stored in memory. This preliminary action enables the biosensor to automatically adjust measurements based on manufacturing variations, providing accurate real-time analytics without requiring post-manufacturing calibration procedures.
Solution Approach 2:
The system implements feedback mechanisms where biosensor measurements are continuously compared against reference values and calibration parameters stored in memory. The system automatically adjusts measurement algorithms based on this feedback, improving the reliability of real-time analytics and glucose concentration forecasting while maintaining measurement precision.
2Manufacturing precision
If traditional manufacturing methods are used, then production cost is reduced, but manufacturing precision and consistency of biosensor performance are compromised
Solution Approach 1:
The manufacturing process performs preliminary characterization of each biosensor component (electrodes, substrates, materials) during fabrication, embedding production data and calibration parameters directly into the device memory. This preliminary action ensures consistent performance across mass-produced biosensors without requiring complex post-manufacturing calibration procedures, thus maintaining manufacturing precision while controlling device complexity.
Solution Approach 2:
The system changes physical and chemical parameters of biosensor components during manufacturing (temperature, humidity, pressure, material composition) and records these parameters in memory. This approach allows the biosensor to compensate for manufacturing variations, ensuring consistent performance across mass production while using standard manufacturing processes.
3Measurement precision
If biosensors with embedded production data and calibration parameters are manufactured, then measurement accuracy and personalized healthcare capabilities are improved, but manufacturing complexity and initial production cost increase
Solution Approach 1:
The manufacturing process merges component fabrication, production data collection, and calibration parameter embedding into a single integrated workflow. Production data (temperature, humidity, pressure, material composition) is collected during manufacturing and stored in the biosensor memory along with calibration parameters, eliminating the need for separate calibration steps and simplifying the overall manufacturing process while maintaining measurement accuracy.
Solution Approach 2:
The biosensor is designed to be self-calibrating by automatically using its embedded production data and calibration parameters to adjust measurements. This self-service capability eliminates the need for complex external calibration procedures, improving measurement accuracy while making the manufacturing process more straightforward by removing post-manufacturing calibration requirements.
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.