Blood Analyte Sensor Error Correction Model
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Solution Overview
Problem
Conventional implantable blood glucose sensors face challenges in accurately measuring blood analyte levels due to unmodeled user-specific and context-specific variables, leading to errors that cannot be pre-programmed or adapted for effectively, resulting in reduced accuracy and reliability.
Innovation Solution
An apparatus and method that utilize a training mode to collect data and generate an error correction operational model, which is applied during normal operation to correct for unmodeled system variables, improving the accuracy of blood analyte level detection and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional implantable sensors are used with pre-programmed error correction, then device complexity is reduced and ease of operation is improved, but measurement precision deteriorates due to inability to account for unmodeled user-specific variables
Solution Approach 1:
The system performs preliminary data collection and model generation during a training mode period before normal operation. Error correction models are developed in advance based on individual user data, then applied during subsequent measurements to improve accuracy without adding complexity to the main measurement function
Solution Approach 2:
The sensor system automatically collects user-specific data and generates personalized error correction models without requiring external intervention. The system self-adapts to individual user characteristics, eliminating the need for manual calibration or complex user setup procedures
2Reliability
If pre-programmed error correction is used, then ease of manufacture and device simplicity are maintained, but reliability deteriorates due to inability to adapt to context-specific variables
Solution Approach 1:
The error correction system transitions from static pre-programmed values to dynamic adaptive models that evolve based on collected user data. The system continuously learns and adjusts correction parameters to match the specific user's physiological characteristics and contextual variables
Solution Approach 2:
The system changes correction parameters based on collected data, transforming fixed error correction values into variable parameters that adapt to individual user characteristics. This allows the same hardware to provide reliable measurements across diverse user populations by adjusting software parameters
3Measurement precision
If training mode data collection is implemented, then measurement precision is improved through personalized error correction, but loss of time occurs during the training period
Solution Approach 1:
The system implements a limited-duration training mode that collects only the minimum necessary data to generate effective error correction models. Rather than requiring extensive training periods, the system uses partial data collection strategies that achieve sufficient model accuracy in reduced time
Solution Approach 2:
The training mode performs preliminary data collection and model generation before normal operation begins. This upfront investment of time during training mode enables significantly improved measurement precision during the much longer normal operation period, making the time loss acceptable and manageable
Data Source
AI summary
Apparatus and methods for error modeling and correction in a blood analyte sensor or system. In one exemplary embodiment, the apparatus employs: (i) a training mode of operation, whereby the apparatus conducts “machine learning” to model one or more errors (e.g., unmodeled variable system errors) associated with the blood analyte measurement process, and (ii) generation of an operational model (based at least in part on data collected/received in the training mode), which is applied to correct or compensate for the errors during normal operation and collection of blood analyte data. This enhances device signal stability and accuracy over extended periods, thereby enabling among other things extended periods of blood analyte sensor implantation, and “personalization” of the sensor apparatus to each user receiving an implant. In one variant, the blood analyte is glucose, and the implanted sensor utilizes an oxygen-based molecular measurement principle.