Non-Invasive Analyte Database for Continuous Monitoring
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Solution Overview
Problem
Current non-invasive analyte sensors using radio or microwave frequency bands struggle to establish accurate and reliable analyte databases for long-term monitoring and prediction of conditions in human or animal subjects, animate or inanimate materials, due to issues with temporary variations and aberrations, and lack of comprehensive data analysis for abnormal or normal conditions.
Innovation Solution
Establishing an analyte database using non-invasive analyte sensors that collect data over a sufficient period to minimize temporary variations, employing spectroscopic techniques in radio or microwave frequency ranges, and updating the database with new data for continuous analysis and prediction of conditions, including medical pathologies or material anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-invasive analyte sensors collect data over a sufficient period to eliminate temporary variations, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary data collection over an extended period to establish a baseline analyte database that captures normal variations. This preliminary action creates a reference framework that enables faster subsequent analysis without sacrificing precision, as the time-consuming data gathering phase is completed in advance.
Solution Approach 2:
The system collects analyte data over an excessively long period beyond what would be minimally required, intentionally gathering more data than necessary to ensure all temporary variations and aberrations are captured. This excessive data collection creates a robust database that improves measurement precision while the analysis system learns to efficiently process only the essential information.
2Reliability
If analyte database is established using data from multiple subjects to improve reliability, then reliability is improved, but device complexity increases
Solution Approach 1:
The system focuses on collecting data from subjects that are homogeneous in relevant characteristics (e.g., similar age ranges, health conditions, or physiological parameters). This homogeneity reduces the complexity of managing diverse data sets while maintaining database reliability, as the data follows more predictable patterns and requires less complex normalization and adjustment procedures.
Solution Approach 2:
The analyte database system is designed to handle multiple types of analyte data from various subjects using a unified data structure and analysis framework. This universal approach allows the same system to process data from different subjects and analyte types without requiring separate complex management systems for each, thereby improving reliability through diverse data while controlling overall system complexity.
3Productivity
If non-invasive sensors use radio or microwave frequency bands for continuous monitoring, then productivity is improved, but loss of information increases due to temporary variations and aberrations
Solution Approach 1:
The system implements feedback mechanisms where collected analyte data is continuously analyzed and used to adjust future data collection parameters. When temporary variations or aberrations are detected, the feedback loop triggers re-sampling or extended monitoring periods for specific analytes, allowing the system to maintain high productivity while recovering potentially lost information through adaptive response to data quality issues.
Solution Approach 2:
The non-invasive sensors perform periodic measurements at scheduled intervals rather than continuous monitoring, which maintains productivity by reducing data overload. The periodic action is strategically designed with varying intervals - more frequent sampling during periods when temporary variations are expected and less frequent sampling when stability is confirmed - thereby minimizing information loss while maintaining efficient operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate and continuous monitoring and prediction of analyte conditions, reducing false positives and negatives by accounting for natural variations and providing comprehensive insights into analyte presence and concentration over time.
Implementation Method 1
employing spectroscopic techniques in radio or microwave frequency ranges
Implementation Method 2
sensors that use radio or microwave frequency bands of the electromagnetic spectrum for non-invasive collection of analyte data
Data Source
AI summary
Establishing an analyte database using analyte data that has been obtained using non-invasive analyte sensors, and using the analyte database to analyze data obtained using a non-invasive analyte sensor. Once the analyte database is established, the analyte database can be updated with new analyte data, and the analyte database can be used to analyze the new analyte data to derive information from the new analyte data. For example, in the case of a human target, the new analyte data together with the analyte database can be used to predict an actual or possible abnormal medical pathology of the human target.